Abnormal data processing system and abnormal data processing method

ABSTRACT

The abnormal data processing system is provided with: a storage unit for holding a multiple-subject DB in which data on multiple subjects are accumulated and individual-subject DB in which data on individual subjects are accumulated; an individual-subject DB divergence-degree calculation unit for calculating an individual-subject DB divergence degree which is the degree of divergence of the new data from the individual-subject DB; a multiple-subject DB divergence degree calculation unit for calculating a multiple-subject DB divergence degree which is the degree of divergence of the new data from the multiple-subject DB; and a composite divergence degree calculation unit for determining a composite divergence by compositing the individual-subject DB divergence degree and the multiple-subject DB divergence degree using the number of data instances in the individual-subject DB. The abnormal data processing system determines whether or not the new data is abnormal on the basis of the composite divergence degree.

TECHNICAL FIELD

The present invention relates to an information processing servicetechnique. Further, the invention relates to a technique for achievingabnormal data processing.

BACKGROUND ART

In fields, such as a healthcare field, a medical field, and a nursingfield, human data measurement systems have increased. These systemscalculate analysis results from the obtained data and feed thecalculation results back to the users to provide value to the users. Asan example of the system, there is a system (finger tap measurement andanalysis system) which measures and analyzes a finger tapping movementof the user to simply evaluate a cognitive function or a movementfunction (for example, Patent Document 1). Here, the finger tappingmovement is a movement of repeatedly opening and closing the thumb andthe index finger. It is known that the performance of the finger tappingmovement varies depending on the presence or absence and severity ofcerebral dysfunction, such as dementia and Parkinson's disease. It hasbeen pointed out that evaluation, such as early detection or severityestimation of the cerebral dysfunction of the user, is likely to beperformed from the analysis results of the finger tapping movement bythe above-described system.

CITATION LIST Patent Document

Patent Document 1: JP 2017-140424 A

Patent Document 2: JP 2013-535268 W

Patent Document 3: JP 2013-039344 A

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

When a human data measurement service typified by a finger tappingmeasurement analysis system spreads widely to general households, it isassumed that the measurement is performed by the user alone or by afamily who is unfamiliar with the measurement with assistance. When datais measured without a skilled measurer, unreliable data (hereinafter,referred to as “abnormal data”) is likely to be measured due to, forexample, an inappropriate measurement procedure or the unexpected actionof the user.

For example, when the above-mentioned finger tapping measurement andanalysis system is given as an example, the following cases areconsidered: a case in which the user interrupts the finger tappingmovement during a predetermined measurement time; and a case in whichthe user misunderstands an instruction and performs the finger tappingmovement.

When the abnormal data is used, there is a problem that it is difficultto feed a highly reliable analysis result back to the user. For example,in the case of the above-mentioned finger tapping measurement andanalysis system, it is considered that the performance of the fingertapping movement is regarded as being lower than the actual situationand analysis results indicating that the possibility of cerebraldysfunction is high are obtained even though the possibility of cerebraldysfunction is low originally.

Therefore, a mechanism for automatically processing abnormal data isrequired. The following cases are considered as approaches to achievethe abnormal data processing mechanism: (A) a case in which abnormalitycan be detected only by focusing on target data; and (B) a case in whichabnormality can be detected for the first time as compared to the pastdatabase (DB). In the case of (A), since it is only necessary to havetarget data, abnormality can be detected by a personal computer (PC)terminal (local PC) connected to a local measurement device. In the caseof (B), when the past DB has been stored in a local PC, abnormality canbe detected by the local PC. However, when the past DB has been storedin a cloud server, abnormality needs to be detected by the server.

The case of (B) will be described in detail. When the target data iscompared with the past DB, the following DBs are considered as the pastDB: (i) a DB (hereinafter, referred to as an “individual-subject DB”)including only the data of the corresponding user; and (ii) a DB(hereinafter, referred to as a “multiple-subject DB”) including the dataof many other users. Since the characteristics of the data of thecorresponding user are reflected in the individual-subject DB of (i), itis desirable to use the individual-subject DB of (i) in order toaccurately detect abnormal data. However, in a case in which the userhas already performed the measurement many times, the individual-subjectDB of (i) can be used. In a case in which the user has performed themeasurement for the first time or only a small number of times, it isnecessary to use the multiple-subject DB of (ii) since the DB of (i) isnot sufficiently accumulated.

In a case in which the multiple-subject DB of (ii) is used, it is onlynecessary to accumulate the data of users other than the correspondinguser in advance. Therefore, there is an advantage that it is easy toprepare the DB. However, the multiple-subject DB of (ii) is an aggregateof the data of many users and the characteristics of the data of thecorresponding user is not reflected in the multiple-subject DB.Therefore, this configuration has a disadvantage that the accuracy ofdetecting abnormal data is likely to be lower than that in a case inwhich the individual-subject DB of (i) is used.

From the awareness of the above-mentioned problems, it is consideredthat a technique which complements the advantages and disadvantages ofthe individual-subject DB of (i) and the multiple-subject DB of (ii) anduses the two DBs together is needed. Examples of the related art relatedto abnormal data processing include JP 2013-535268 W (Patent Document 2)and JP 2013-039344 A (Patent Document 3). However, these two patentdocuments merely disclose a method for specifying whether or not testdata is abnormal for a database which has been given in advance and donot disclose a technique that improves the accuracy of detectingabnormal data using both the individual-subject DB and themultiple-subject DB. Therefore, the invention proposes a technique thatautomatically processes abnormal data with high accuracy.

Solutions to Problems

According to an aspect of the invention, there is provided an abnormaldata processing system that detects whether or not new data is abnormaland processes the new data. The abnormal data processing systemincludes: a storage unit that stores a multiple-subject DB in which dataof a plurality of subjects is accumulated and an individual-subject DBin which data of an individual subject is accumulated; anindividual-subject DB divergence degree calculation unit that calculatesan individual-subject DB divergence degree which is a degree ofdivergence of the new data from the individual-subject DB; amultiple-subject DB divergence degree calculation unit that calculates amultiple-subject DB divergence degree which is a degree of divergence ofthe new data from the multiple-subject DB; and a composite divergencedegree calculation unit that calculates a composite degree of divergenceobtained by combining the individual-subject DB divergence degree andthe multiple-subject DB divergence degree, using the number of dataitems in the individual-subject DB. It is determined whether or not thenew data is abnormal on the basis of the composite degree of divergence.

There is provided an abnormal data processing method that detectswhether or not new data acquired by an input unit is abnormal andprocesses the new data, using the input unit, an output unit, a controlunit, and a storage unit. The abnormal data processing method includes:storing a multiple-subject DB in which data of a plurality of subjectsis accumulated and an individual-subject DB in which data of anindividual subject is accumulated in the storage unit; calculating anindividual-subject DB divergence degree which is a degree of divergenceof the new data from the individual-subject DB; calculating amultiple-subject DB divergence degree which is a degree of divergence ofthe new data from the multiple-subject DB; calculating a compositedegree of divergence using the individual-subject DB divergence degreeand the multiple-subject DB divergence degree, on the basis of thenumber of data items in the individual-subject DB; and determiningwhether or not the new data is abnormal on the basis of the compositedegree of divergence.

Effects of the Invention

It is possible to achieve a technique that automatically processesabnormal data with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the configuration of an abnormaldata processing system according to Embodiment 1 of the presentinvention.

FIG. 2 is a block diagram illustrating the configuration of the abnormaldata processing system in Embodiment 1.

FIG. 3 is a block diagram illustrating the configuration of ameasurement device in Embodiment 1.

FIG. 4 is a block diagram illustrating the configuration of a terminaldevice in Embodiment 1.

FIG. 5 is a perspective view illustrating a motion sensor attached to afinger in Embodiment 1.

FIG. 6 is a block diagram illustrating the configuration of a motionsensor control unit and the like of the measurement device in Embodiment1.

FIG. 7 is a flowchart illustrating a process flow of the abnormal dataprocessing system in Embodiment 1.

FIG. 8 is a waveform diagram illustrating an example of waveform signalsof feature amounts in Embodiment 1.

FIG. 9 is a table illustrating an example of the configuration of afeature amount list in Embodiment 1.

FIG. 10 is a waveform diagram illustrating an example of non-DB-usingabnormal data detection in Embodiment 1.

FIG. 11 is a conceptual diagram illustrating abnormal data detectionusing an individual-subject DB and abnormal data detection using amultiple-subject DB in Embodiment 1.

FIG. 12 is a conceptual diagram illustrating a method for calculating acomposite degree of divergence in Embodiment 1.

FIG. 13 is a conceptual diagram illustrating a method for calculating adegree of divergence in consideration of a time series in Embodiment 1.

FIG. 14 is a table illustrating an example of the configuration of anabnormality detection reason-processing correspondence table inEmbodiment 1.

FIG. 15 is a plan view illustrating a menu screen as an example of adisplay screen in Embodiment 1.

FIG. 16 is a plan view illustrating a task measurement screen as anexample of the display screen in Embodiment 1.

FIG. 17 is a plan view illustrating an evaluation result screen as anexample of the display screen in Embodiment 1.

FIG. 18 is a plan view illustrating a first abnormal data detection andprocessing screen as an example of the display screen in Embodiment 1.

FIG. 19 is a plan view illustrating a second abnormal data detection andprocessing screen as an example of the display screen in Embodiment 1.

FIG. 20 is a block diagram illustrating the configuration of an abnormaldata processing system according to Embodiment 2 of the invention.

FIG. 21 is a plan view illustrating a finger tap on a screen as anexample of movement in Embodiment 2.

FIG. 22 is a waveform diagram illustrating the waveform of a distancebetween two fingers of the finger tap on the screen in Embodiment 2.

FIG. 23 is a plan view illustrating reaching as an example of themovement in Embodiment 2.

FIG. 24 is a plan view illustrating a continuous touch as an example ofthe movement in Embodiment 2.

FIG. 25 is a plan view illustrating a tap according to stimulation as anexample of the movement in Embodiment 2.

FIG. 26 is a plan view illustrating a five-finger tap as an example ofthe movement in Embodiment 2.

FIG. 27 is a block diagram illustrating the configuration of an abnormaldata processing system according to Embodiment 3 of the invention.

FIG. 28 is a block diagram illustrating the configuration of a serverwhich is an abnormal data processing system according to Embodiment 3.

FIG. 29 is a table illustrating an example of the configuration of userinformation which is server management information in Embodiment 3.

MODE FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the invention will be described in detailwith reference to the drawings. In all the drawings for describing theembodiments, the same portions are denoted by the same referencenumerals in principle and the description thereof will not be repeated.

The embodiments will be described in detail with reference to thedrawings. The invention is not construed as being limited to thedescription of the following embodiments. It is easily understood bythose skilled in the art that a specific configuration can be changedwithout departing from the spirit or spirit of the invention.

In a case in which there are a plurality of elements having the same orsimilar functions, the same reference numerals may be given differentsubscripts for explanation. However, in a case in which there is no needto distinguish a plurality of elements, the description of the elementsmay be made with suffixes omitted.

For example, notations, such as “first”, “second”, and “third”, in thespecification are used to identify components and do not necessarilylimit the number, order, or content thereof. In addition, numbers foridentifying components are used for each context and numbers used in onecontext do not necessarily indicate the same configuration in anothercontext. Further, a component identified by one number may also have thefunction of a component identified by another number.

For example, in some cases, the position, size, shape, and range of eachcomponent illustrated in the drawings are different from the actualposition, size, shape, and range for ease of understanding of theinvention. Therefore, the invention is not necessarily limited to, forexample, the position, size, shape, and range disclosed in the drawings.

In this embodiment, a technique for automatically processing abnormaldata is proposed. In the processing of abnormal data, there are thefollowing cases: (A) a case in which abnormality can be detected only byfocusing on target data; and (B) a case in which abnormality can bedetected for the first time as compared to the past DB. In thisembodiment, particularly, for (B), a technique is proposed which detectsabnormal data using both (i) a DB (individual-subject DB) including onlythe data of the corresponding user and (ii) a DB (multiple-subject DB)including the data of many other users. According to a typicalembodiment, (i) a decrease in accuracy due to an insufficient amount ofdata when the DB including only of the data of the user is used and adecrease in accuracy due to incapability to reflect an individualdifference when the DB including the data of many other users is usedare complemented to detect abnormal data with high accuracy.

Embodiment 1

An abnormal data processing system according to Embodiment 1 of theinvention will be described with reference to FIGS. 1 to 19 . Theabnormal data processing system according to Embodiment 1 has a functionof detecting abnormality in data measured by a user and generatingprocessing content in a case in which abnormality is detected. Thisfunction makes it possible to detect abnormal data with high accuracy.

[Human Data Measurement System (1)]

FIG. 1 illustrates the configuration of a human data measurement systemincluding the abnormal data processing system according to Embodiment 1.In Embodiment 1, a human data measurement system is provided in afacility, such as a hospital or a facility for the elderly, or in theuser's home. The human data measurement system includes an abnormal dataprocessing system 1 and a measurement system 2 which is amagnetic-sensor-type finger tapping movement system and these systemsare connected to each other through a communication line. Themeasurement system includes a measurement device 3 and a terminal device4 which are connected to each other through a communication line. Aplurality of measurement systems 2 may be provided in the facility.

The measurement system 2 is a system that measures finger movement usinga magnetic-sensor-type motion sensor. A motion sensor is connected tothe measurement device 3. The motion sensor is attached to the user'sfinger. The measurement device 3 measures finger movement through themotion sensor to obtain measurement data including a time-serieswaveform signal.

The terminal device 4 displays various kinds of information for abnormaldata processing including the detection result of abnormal data, areason for abnormality detection, and the content of abnormal dataprocessing on a display screen and receives an operation input by theuser. In Embodiment 1, the terminal device 4 is a PC.

The abnormal data processing system 1 has a function of providing anabnormal data processing service as an information processing service.For example, the abnormal data processing system 1 has, as itsfunctions, an abnormal data detection function and an abnormal dataprocessing determination function. The abnormal data detection functionis a function of detecting whether or not measurement data measured bythe measurement system 2 is abnormal. The abnormal data processingdetermination function is a function of determining a process for thedata in which abnormality has been detected by the abnormal datadetection function.

The abnormal data processing system 1 receives, for example, the contentof an instruction to the user and measurement data as input data fromthe measurement system 2. The abnormal data processing system 1 outputs,for example, the detection result of abnormal data and the content ofabnormal data processing as output data to the measurement system 2. Thedetection result of abnormal data includes a reason for abnormal datadetection in addition to whether or not the measurement data isabnormal.

The human data measurement system according to Embodiment 1 can bewidely applied to general facilities and people in addition tofacilities, such as hospitals and facilities for the elderly, andsubjects in the facilities. The measurement device 3 and the terminaldevice 4 may be integrated into a measurement system. The measurementsystem 2 and the abnormal data processing system 1 may be integratedinto an apparatus. The terminal device 4 and the abnormal dataprocessing system 1 may be integrated into an apparatus. The measurementdevice 3 and the abnormal data processing system 1 may be integratedinto an apparatus.

[Abnormal Data Processing System]

FIG. 2 illustrates the configuration of the abnormal data processingsystem 1 according to Embodiment 1. The abnormal data processing system1 includes, for example, a control unit 101, a storage unit 102, aninput unit 103, an output unit 104, and a communication unit 105 whichare connected to each other through a bus. The input unit 103 is aportion that inputs an operation of, for example, the administrator ofthe abnormal data processing system 1. The output unit 104 is a portionthat displays a screen to, for example, the administrator of theabnormal data processing system 1. The communication unit 105 has acommunication interface and performs a communication process with themeasurement device 3 and the terminal device 4.

The control unit 101 controls the entire abnormal data processing systemand includes, for example, a central processing unit (CPU), a read onlymemory (ROM), a random access memory (RAM) and implements a dataprocessing unit that performs, for example, abnormal data detection orabnormal data processing determination on the basis of software programprocessing. A data processing unit of the control unit 101 includes auser information management unit 11, a task processing unit 12, ananalysis evaluation unit 13, an abnormal data detection unit 14, anabnormal data processing determination unit 15, an abnormal dataprocessing execution unit 16, and a result output unit 17. The controlunit 101 implements, for example, a function of receiving measurementdata from the measurement device 3, a function of processing andanalyzing the measurement data, a function of outputting a controlinstruction to the measurement device 3 or the terminal device 4, and afunction of outputting display data to the terminal device 4.

The user information management unit 11 performs, for example, a processof registering user information input by the user in user information 41of a DB 40 and managing the user information and a process of checkingthe user information 41 of the DB 40 when the user uses a service. Theuser information 41 includes, for example, attribute values, usagehistory information, user setting information for each user. Theattribute values include, for example, sex and age. The usage historyinformation is information for managing the history of the user usingthe service provided by the system. The user setting information issetting information set by the user for the function of the service.

The task processing unit 12 is a portion that performs a process relatedto a task for analyzing and evaluating, for example, a movementfunction. In other words, the task is a predetermined finger movement.The task processing unit 12 outputs a task to the screen of the terminaldevice 4 on the basis of task data 42A in the DB 40. Further, the taskprocessing unit 12 acquires the measurement data of the task measured bythe measurement device 3 and stores the measurement data as measurementdata 42B in the DB 40.

The analysis evaluation unit 13 is a portion that calculates a featureamount indicating the property of the measurement data on the basis ofthe measurement data 42B of the user. The analysis evaluation unit 13stores analysis evaluation data 43 which is the result of an analysisevaluation process in the DB 40.

The abnormal data detection unit 14 performs a process of detectingabnormal data and a process of outputting abnormal data detection result44 to the screen of the terminal device 4, on the basis of the analysisevaluation data 43 of the user, an individual-subject DB 45A, amultiple-subject DB 45B, and information of an abnormality detectionreason-processing correspondence table 50B in a management table 50. Theabnormal data detection unit 14 stores the detection result ofabnormality as the abnormal data detection result 44 in the DB 40. Theabnormal data detection result 44 includes the reason for abnormal datadetection in addition to whether or not the measurement data isabnormal. The abnormal data detection unit 14 transmits the abnormaldata detection result 44 of the DB 40 to the terminal device 4 so as tobe output to the screen. The abnormal data detection unit 14 includes anon-DB-using abnormal data detection unit 14A that detects abnormal datawithout using a DB and a DB-using abnormal data detection unit 14B thatdetects abnormal data using a DB. The DB-using abnormal data detectionunit 14B includes an individual-subject DB divergence degree calculationunit 14Ba, a multiple-subject DB divergence degree calculation unit14Bb, and a composite divergence degree calculation unit 14Bc.

The abnormal data processing determination unit 15 performs, forexample, a process of creating abnormal data processing content 46 onthe basis of the abnormal data detection result 44 and the abnormalitydetection reason-processing correspondence table 50B in the managementtable 50 and storing the abnormal data processing content 46 in the DB40.

The abnormal data processing execution unit 16 performs, for example, aprocess of executing abnormal data processing content on the basis ofthe abnormal data processing content 46. At this time, the measurementdata 42B may or may not be stored in the individual-subject DB 45A orthe multiple-subject DB 45B according to the abnormal data processingcontent 46.

Here, the individual-subject DB 45A is a DB including only the data of aspecific user and the multiple-subject DB 45B is a DB obtained bycombining the data of a plurality of users. In general, the data of theindividual-subject DB 45A is a subset of the multiple-subject DB 45B.However, the data of the individual-subject DB 45A may not benecessarily included in the data of the multiple-subject DB 45B. Inaddition, the individual-subject DB 45A and the multiple-subject DB 45Bmay be separated as databases. However, information for specifying auser may be attached to the data of the multiple-subject DB 45B suchthat the data of a specific user can be extracted. In this case, themultiple-subject DB 45B can also have the functions of theindividual-subject DB 45A.

The result output unit 17 performs a process of outputting the analysisand evaluation data 43 of the user, the abnormal data detection result44, and the abnormal data processing content 46 to the screen of theterminal device 4. The analysis evaluation unit 13, the abnormal datadetection unit 14, the abnormal data processing determination unit 15,and the abnormal data processing execution unit 16 perform a screenoutput process in cooperation with the result output unit 17.

Examples of the data and information stored in the DB 40 of the storageunit 102 include the user information 41, the task data 42A, themeasurement data 42B, the analysis evaluation data 43, the abnormal datadetection result 44, the individual-subject DB 45A, the multiple-subjectDB 45B, the abnormal data processing content 46, and the managementtable 50. The control unit 101 stores the management table 50 in thestorage unit 102 and manages the management table 50. The administratorcan set the content of the management table 50. The management table 50stores, for example, a feature amount list 50A for setting featureamounts and the abnormality detection reason-processing correspondencetable 50B for setting a process corresponding to the abnormalitydetection reason.

[Measurement Device]

FIG. 3 illustrates the configuration of the measurement device 3according to Embodiment 1. The measurement device 3 includes, forexample, a motion sensor 20, an accommodation unit 301, a measurementunit 302, and a communication unit 303. The accommodation unit 301includes a motion sensor interface unit 311 to which the motion sensor20 is connected and a motion sensor control unit 312 that controls themotion sensor 20. The measurement unit 302 measures a waveform signalthrough the motion sensor 20 and the accommodation unit 301 and outputsthe measured signal as measurement data. The measurement unit 302includes a task measurement unit 321 that obtains measurement data. Thecommunication unit 303 has a communication interface and communicateswith the abnormal data processing system 1 to transmit measurement datato the abnormal data processing system 1. The motion sensor interfaceunit 311 includes an analog-to-digital conversion circuit and convertsan analog waveform signal detected by the motion sensor 20 into adigital waveform signal using sampling. The digital waveform signal isinput to the motion sensor control unit 312.

The measurement device 3 may store each measurement data item in thestorage unit. Alternatively, the measurement device 3 may not store eachmeasurement data item and only the abnormal data processing system 1 maystore each measurement data item.

[Terminal Device]

FIG. 4 illustrates the configuration of the terminal device 4 accordingto Embodiment 1. The terminal device 4 includes a control unit 401, astorage unit 402, a communication unit 403, an input device 404, and adisplay device 405. The control unit 401 performs, for example, thedisplay of abnormal data detection results, the display of abnormal dataprocessing content, and the execution of abnormal data processingcontent, as control processes based on software program processing. Thestorage unit 402 stores, for example, the user information, the taskdata, the measurement data, the analysis evaluation data, the abnormaldata detection results, and the abnormal data processing contentobtained from the abnormal data processing system 1. The communicationunit 403 has a communication interface and communicates with theabnormal data processing system 1 to receive various kinds of data fromthe abnormal data processing system 1 and to transmit, for example, userinstruction input information to the abnormal data processing system 1.The input device 404 is, for example, a keyboard or a mouse. The displaydevice 405 displays various kinds of information on a display screen406. In addition, the display device 405 may be a touch panel.

[Finger, Motion Sensor, and Finger Tap Measurement]

FIG. 5 illustrates a state in which a magnetic sensor, which is themotion sensor 20, is attached to the user's finger. The motion sensor 20has a transmitting coil unit 21 and a receiving coil unit 22 which arepaired coil units and are connected to the measurement device 3 throughsignal lines 23. The transmitting coil unit 21 generates a magneticfield and the receiving coil unit 22 detects the magnetic field. In theexample illustrated in FIG. 5 , in the right hand of the user, thetransmitting coil unit 21 is attached to the vicinity of the nail of thethumb and the receiving coil unit 22 is attached to the vicinity of thenail of the index finger. The fingers to which the coil units areattached can be changed to other fingers. The place to which the coilunit is attached is not limited to the vicinity of the nail.

As illustrated in FIG. 5 , it is assumed that the motion sensor 20 isattached to the target finger of the user, for example, two fingers ofthe left thumb and the index finger. In this state, the user performs afinger tap which is the repetitive movement of opening and closing twofingers. In the finger tap, movement is performed between a state inwhich two fingers are closed, that is, a state in which the tips of twofingers come into contact with each other, and a state in which twofingers are opened, that is, a state in which the tips of two fingersare opened. The distance between the coil units, that is, thetransmitting coil unit 21 and the receiving coil unit 22 whichcorresponds to the distance between the tips of two fingers is changedby this movement. The measurement device 3 measures a waveform signalcorresponding to a change in the magnetic field between the transmittingcoil unit 21 and the receiving coil unit 22 of the motion sensor 20.

The finger tap includes the following various types of tasks in detail.Examples of the movement include one-handed free-run, one-handedmetronome, two-handed simultaneous free-run, two-handed alternatingfree-run, two-handed simultaneous metronome, two-handed alternatingmetronome. The one-handed free-run means performing finger tapping withtwo fingers of one hand several times as quickly as possible. Theone-handed metronome means performing finger tapping with two fingers ofone hand in synchronization with a constant pace of stimulation. Thetwo-handed simultaneous free-run means performing finger tapping at thesame timing with two fingers of the left hand and two fingers of theright hand. The two-handed alternating free-run means performing fingertapping at an alternate timing with two fingers of the left hand and twofingers of the right hand.

[Motion Sensor Control Unit and Finger Tap Measurement]

FIG. 6 illustrates an example of the detailed configuration of, forexample, the motion sensor control unit 312 of the measurement device 3.In the motion sensor 20, the distance between the transmitting coil unit21 and the receiving coil unit 22 is represented by D. The motion sensorcontrol unit 312 includes an alternating current generation circuit 312a, a current generation amplifier circuit 312 b, a pre-amplifier circuit312 c, a detection circuit 312 d, an LPF circuit 312 e, a phaseadjustment circuit 312 f, an amplifier circuit 312 g, and an outputsignal terminal 312 h. The alternating current generation circuit 312 ais connected to the current generation amplifier circuit 312 b and thephase adjustment circuit 312 f. The transmitting coil unit 21 isconnected to the current generation amplifier circuit 312 b through thesignal line 23. The receiving coil unit 22 is connected to thepre-amplifier circuit 312 c through the signal line 23. The detectioncircuit 312 d, the LPF circuit 312 e, the amplifier circuit 312 g, andthe output signal terminal 312 h are sequentially connected to a stagebehind the pre-amplifier circuit 312 c. The detection circuit 312 d isconnected to the phase adjustment circuit 312 f.

The alternating current generation circuit 312 a generates analternating current voltage signal with a predetermined frequency. Thecurrent generation amplifier circuit 312 b converts the alternatingcurrent voltage signal into an alternating current with a predeterminedfrequency and outputs the alternating current to the transmitting coilunit 21. The transmitting coil unit 21 generates a magnetic field usingthe alternating current. The magnetic field causes the receiving coilunit 22 to generate an induced electromotive force. The receiving coilunit 22 outputs an alternating current generated by the inducedelectromotive force. The frequency of the alternating current is equalto the predetermined frequency of the alternating current voltage signalgenerated by the alternating current generation circuit 312 a.

The pre-amplifier circuit 312 c amplifies the detected alternatingcurrent. The detection circuit 312 d detects the amplified signal on thebasis of a reference signal 312 i from the phase adjustment circuit 312f. The phase adjustment circuit 312 f adjusts the phase of thealternating current voltage signal with a predetermined frequency or afrequency that is twice the predetermined frequency which has beentransmitted from the alternating current generation circuit 312 a andoutputs the alternating current voltage signal as the reference signal312 i. The LPF circuit 312 e limits the band of the detected signal andoutputs the signal. The amplifier circuit 312 g amplifies the signal toa predetermined voltage. Then, an output signal corresponding to themeasured waveform signal is output from the output signal terminal 312h.

The waveform signal which is the output signal is a signal having avoltage value indicating the distance D between two fingers. Thedistance D and the voltage value can be converted on the basis of apredetermined calculation expression. The calculation expression may beobtained by calibration. In the calibration, for example, measurement isperformed in a state in which the user holds a block with apredetermined length with two fingers of a target hand. A predeterminedcalculation expression is obtained as an approximate curve thatminimizes an error from a data set of the voltage value and the distancevalue in the measured value. Further, the size of the user's hand may bechecked by calibration and may be used for, for example, normalizingfeature amounts. In Embodiment 1, the above-mentioned magnetic sensor isused as the motion sensor 20 and a measurement means corresponding tothe magnetic sensor is used. However, the invention is not limitedthereto. For example, other detection means and measurement means, suchas an acceleration sensor, a strain gauge, and a high-speed camera, maybe applied.

[Process Flow]

FIG. 7 illustrates the flow of the entire process which is mainlyperformed by the abnormal data processing system 1 in the human datameasurement system according to Embodiment 1. FIG. 7 has Steps S1 toS10. Hereinafter, the process will be described in the order of thesteps.

(S1) The user operates the measurement system 2. The terminal device 4displays an initial screen on the display screen. The user selects adesired operation item on the initial screen. For example, an operationitem for detecting and processing abnormal data is selected. Theterminal device 4 transmits instruction input information correspondingto the selection to the abnormal data processing system 1. In addition,the user can input and register user information, such as sex and age,on the initial screen. In this case, the terminal device 4 transmits theinput user information to the abnormal data processing system 1. Theuser information management unit 11 of the abnormal data processingsystem 1 registers the user information in the user information 41.

(S2) The task processing unit 12 of the abnormal data processing systemtransmits task data for the user to the terminal device 4 on the basisof the instruction input information of S1 and the task data 42A of thefinger tap. The task data includes one or more kinds of task informationrelated to finger movement, such as one-handed free-run, two-handedsimultaneous free-run, and two-handed alternating free-run. The terminaldevice 4 displays finger movement task information on the display screenon the basis of the received task data. The user performs a fingermovement task according to the task information on the display screen.The measurement device 3 measures the task and transmits the task asmeasurement data to the abnormal data processing system 1. The abnormaldata processing system 1 stores the measurement data in the measurementdata 42B.

(S3) The analysis evaluation unit 13 of the abnormal data processingsystem performs a process of analyzing and evaluating, for example, theuser's movement function on the basis of the measurement data 42B of S2to create the analysis evaluation data 43 of the user and stores theanalysis evaluation data 43 in the DB 40. In the analysis evaluationprocess, the analysis evaluation unit 13 extracts feature amounts on thebasis of the waveform signal of the measurement data 42B of the user.The feature amounts include, for example, feature amounts calculatedfrom a distance waveform which will be described below and featureamounts calculated from a velocity waveform. The feature amounts arerecorded on the feature amount list 50A. The analysis evaluation unit 13may correct the extracted feature amounts on the basis of an attributevalue such as the age of the user. Then, the corrected feature amountsmay be used for evaluation.

(S4) The result output unit 17 of the abnormal data processing system 1outputs analysis evaluation result information to the display screen ofthe terminal device 4 on the basis of the analysis evaluation data 43 ofS3. The user can check the analysis evaluation result informationindicating the state of, for example, his or her movement function onthe display screen. Step S4 may be omitted.

(S5) The non-DB-using abnormal data detection unit 14A in the abnormaldata detection unit 14 of the abnormal data processing system 1 detectsabnormal data without using the DB on the basis of the analysisevaluation data 43 of S3. That is, the non-DB-using abnormal datadetection unit 14A detects abnormality that can be detected only fromthe measurement data, without referring to the individual-subject DB 45Aor the multiple-subject DB 45B which was accumulated in the past. Adetailed detection method will be described below. A list of abnormalitydetection reasons to be detected is recorded on the abnormalitydetection reason-processing correspondence table 50B. Abnormal data isdetected on the basis of the abnormality detection reason. The result isstored in the abnormal data detection result 44 of the storage unit 40.Step S5 may be omitted and only Step S6 may be performed.

(S6) The DB-using abnormal data detection unit 14B in the abnormal datadetection unit 14 of the abnormal data processing system 1 detectsabnormal data using the DB on the basis of the analysis evaluation data43 of S3, the individual-subject DB 45A, and the multiple-subject DB45B. That is, the measurement data is compared with the pastindividual-subject DB or multiple-subject DB to detect abnormality thatcan be detected first. A detailed detection method will be describedbelow. A list of abnormality detection items to be detected is recordedon the abnormality detection reason-processing correspondence table 50B.Abnormal data is detected on the basis of the abnormality detectionitem. The result is stored in the abnormal data detection result 44 ofthe storage unit 40. Step S6 may be omitted and only Step S5 may beperformed.

(S7) The abnormal data processing determination unit 15 of the abnormaldata processing system 1 determines the abnormal data processing content46 on the basis of the abnormal data detection result 44 generated inSteps S5 and S6. A detailed determination method will be describedbelow. A correspondence table between the abnormal data detection result44 and the abnormal data processing content 46 is recorded on theabnormality detection reason-processing correspondence table 50B. Theabnormal data processing determination unit 15 determines abnormal dataprocessing content on the basis of the correspondence table and storesthe abnormal data processing content in the abnormal data processingcontent 46.

(S8) In the abnormal data processing system 1, the analysis andevaluation unit 13 displays the abnormal data detection result 44generated in S5 and S6 and the abnormal data processing content 46generated in S7 on the display screen. The user can check the currentdetection result of abnormal data and the processing content thereof onthe display screen.

(S9) The abnormal data processing system 1 performs abnormal dataprocessing on the basis of the abnormal data processing content 46. Asan example, a process is considered which stores the abnormal dataprocessing content in the individual-subject DB 45A and themultiple-subjects DB 45B in a case in which abnormality has not beendetected and does not store the abnormal data processing content in acase in which abnormality has been detected. As another example, aprocess is considered which outputs the abnormal data processing content46 to the terminal device 4 through the communication unit 105 torequest re-measurement. A detailed execution method will be describedbelow.

(S10) In a case in which the re-measurement request is transmitted tothe terminal device 4 in S9, the abnormal data processing system 1returns to S2 and repeats the process in the same manner. In a case inwhich the re-measurement request is not transmitted, the flow ends.

[Feature Amounts]

FIG. 8 illustrates an example of the waveform signals of the featureamounts. In FIG. 8 , (a) illustrates a waveform signal of the distance Dbetween two fingers, (b) illustrates a waveform signal of the velocityof two fingers, and (c) illustrates a waveform signal of theacceleration of two fingers. The velocity in (b) is obtained by the timedifferentiation of the waveform signal of the distance in (a). Theacceleration in (c) is obtained by the time differentiation of thewaveform signal of the velocity in (b). The analysis evaluation unit 13obtains the waveform signals of predetermined feature amounts in thisexample from the waveform signal of the measurement data 42B on thebasis of an operation, such as differentiation or integration. Further,the analysis evaluation unit 13 obtains predetermined calculated valuesfrom the feature amounts.

(d) of FIG. 8 is an enlarged view of (a) and illustrates an example ofthe feature amount. (d) of FIG. 8 illustrates, for example, a maximumvalue D max of the finger tap distance D and a tap interval TI. Ahorizontal dashed line indicates an average value Dav of the distance Dover the entire measurement time. The maximum value D max indicates themaximum value of the distance D over the entire measurement time. Thetap interval TI is the time corresponding to the cycle TC of one fingertap and particularly indicates the time from a local minimum point P minto the next local minimum point P min. In addition, (d) of FIG. 8illustrates a local maximum point P max, the local minimum point P minwithin one cycle of the distance D, the time T1 of an opening operationwhich will be described below, and the time T2 of a closing operationwill be described below.

Hereinafter, a detailed example of the feature amounts will bedescribed. In Embodiment 1, a plurality of feature amounts obtained fromthe waveforms of the distance, the velocity, and the acceleration areused. In other embodiments, only some of the plurality of featureamounts may be used or other feature amounts may be used. The details ofthe definition of the feature amounts are not limited.

FIG. 9 illustrates a feature amount [distance] among the finger tapfeature amounts recorded on the feature amount list 50A. The setting ofthis association is an example and can be changed. The feature amountlist 50A illustrated in FIG. 9 has a feature amount classificationcolumn, an identification number column, and a feature amount parametercolumn. The feature amount classification includes [distance],[velocity], [acceleration], [tap interval], [phase difference], and[marker following]. For example, the feature amount [distance] has aplurality of feature amount parameters identified by identificationnumbers (1) to (7). Units are illustrated in parentheses [ ] of thefeature parameters.

(1) A “maximum amplitude of the distance” [mm] is the difference betweenthe maximum value and the minimum value of the amplitude in the waveformof the distance ((a) of FIG. 8 ). (2) A “total movement distance” [mm]is the total sum of the absolute values of the amounts of change in thedistance during the entire measurement time of one measurementoperation. (3) “Average of the local maximum points of the distance”[mm] is the average value of the values of the local maximum points ofthe amplitude in each cycle. (4) A “standard deviation of the maximumpoint of the distance” [mm] is the standard deviation of theabove-mentioned value.

(5) A “slope (attenuation rate) of an approximate curve at the localmaximum point of the distance” [mm/sec] is the slope of a curveapproximating the local maximum point of the amplitude. This parametermainly indicates a change in the amplitude due to fatigue during themeasurement time. (6) A “variation coefficient of the local maximumpoint of the distance” is a variation coefficient of the local maximumpoint of the amplitude and the unit of the variation coefficient is adimensionless quantity (represented by [−]). This parameter is a valueobtained by normalizing the standard deviation with an average value.Therefore, it is possible to exclude an individual difference in thelength of the finger. (7) A “standard deviation of a local maximum pointof the distance” [mm] is the standard deviation of three adjacent localmaximum points of the amplitude. This parameter is a parameter forevaluating the degree of local variation in amplitude in a short time.

Hereinafter, each feature amount parameter (not illustrated) will bedescribed. The feature amount [velocity] has feature amount parametersindicated by the following identification numbers (8) to (22). (8) A“maximum amplitude of the velocity” [m/sec] is the difference betweenthe maximum value and the minimum value of the velocity in the waveformof the velocity ((b) of FIG. 8 ). (9) “Average of the local maximumpoints of an opening velocity” [m/s] is an average value related to themaximum value of the velocity at the time of an opening operation ineach finger tap waveform. The opening operation is an operation ofchanging two fingers from a closed state to a maximum open state ((d) inFIG. 8 ). (10) “Average of the local maximum points of a closingvelocity” [m/sec] is an average value related to the local maximum valueof the velocity at the time of a closing operation. The closingoperation is an operation of changing two fingers from the maximum openstate to the closed state. (11) A “standard deviation of the localmaximum points of the opening velocity” [m/sec] is a standard deviationrelated to the maximum value of the velocity at the time of the openingoperation. (12) “Average of the local maximum points of the closingvelocity” [m/sec] is a standard deviation related to the local maximumvalue of the velocity at the time of the closing operation.

(13) An “energy balance” [−] is the ratio of the sum of squares of thevelocity during the opening operation to the sum of squares of thevelocity during the closing operation. (14) “Total energy” [m²/sec²] isthe sum of squares of the velocity for the entire measurement time. (15)A “variation coefficient of the local maximum point of the openingvelocity” [−] is a variation coefficient relating to the maximum valueof the velocity at the time of the opening operation and is a valueobtained by normalizing the standard deviation with an average value.(16) “Average of the local maximum points of the closing velocity”[m/sec] is a variation coefficient related to the minimum value of thevelocity at the time of the closing operation.

(17) The “number of shakes” [−] is a value obtained by subtracting thenumber of large opening and closing finger taps from the number ofreciprocating movements in which the sign of the waveform of thevelocity changes. (18) “Average of a distance ratio at the peak of theopening velocity” [−] is the average value of a distance ratio at themaximum value of the velocity during the opening operation in a case inwhich the finger tap amplitude is 1.0. (19) “Average of a distance ratioat the peak of the closing velocity” [−] is the average value of thedistance ratio at the maximum value of the velocity during the closingoperation in a case in which the finger tap amplitude is 1.0. (20) A“ratio of the distance ratios at the peak of the velocity” [−] is theratio of the value of (18) to the value of (19). (21) A “standarddeviation of the distance ratio at the peak of the opening velocity” [−]is a standard deviation related to the distance ratio at the maximumvalue of the velocity during the opening operation in a case in whichthe finger tap amplitude is 1.0. (22) A “standard deviation of thedistance ratio at the peak of the closing velocity” [−] is a standarddeviation related to the distance ratio at the minimum value of thevelocity during the closing operation in a case in which the finger tapamplitude is 1.0.

The feature amount [acceleration] has feature amount parametersindicated by the following identification numbers (23) to (32). (23) A“maximum amplitude of acceleration” [m/sec²] is the difference betweenthe maximum value and the minimum value of the acceleration in thewaveform of the acceleration ((c) in FIG. 8 ). (24) “Average of thelocal maximum points of opening acceleration” [m/sec²] is the average ofthe maximum values of the acceleration during the opening operation andis a first value among four types of extreme values that appear in onecycle of the finger tap. (25) “Average of the local minimum points ofopening acceleration” [m/sec²] is the average of the local minimumvalues of the acceleration during the opening operation and is a secondvalue among the four types of extreme values. (26) “Average of the localmaximum points of closing acceleration” [m/sec²] is the average of thelocal maximum values of the acceleration during the closing operationand is a third value among the four types of extreme values. (27)“Average of the local minimum points of the closing acceleration”[m/sec²] is the average of the local minimum values of the accelerationduring the closing operation and is a fourth value among the four typesof extreme values.

(28) “Average of contact time” [sec] is the average value of the contacttime in the closed state of two fingers. (29) A “standard deviation ofthe contact time” [sec] is the standard deviation of the contact time.(30) A “variation coefficient of the contact time” [−] is a variationcoefficient of the contact time. (31) The “number of zero crossings ofacceleration” [−] is the average number of times the sign ofacceleration changes in one cycle of the finger tap. This value isideally two. (32) The “number of times movement is frozen” [−] is avalue obtained by subtracting the number of large opening and closingfinger taps from the number of reciprocating movements in which the signof acceleration changes in one cycle of the finger tap.

The feature amount [tap interval] has feature amount parametersindicated by the following identification numbers (33) to (41). (33) The“number of taps” [−] is the number of finger taps for the entiremeasurement time in one measurement operation. (34) An “average tapinterval” [sec] is an average value of the above-mentioned tap interval((d) in FIG. 8 ) in the waveform of the distance. (35) A “tap frequency”[Hz] is the frequency at which a spectrum is the maximum in a case inwhich Fourier transform is performed for the waveform of the distance.(36) A “tap interval standard deviation” [sec] is a standard deviationrelated to the tap interval.

(37) A “tap interval variation coefficient” [−] is a variationcoefficient related to the tap interval and is a value obtained bynormalizing the standard deviation with an average value. (38) A “tapinterval variation” [mm²] is an integrated value at which the frequencyis in the range of 0.2 to 2.0 Hz in a case in which spectrum analysis isperformed for the tap interval. (39) “Skewness of a tap intervaldistribution” [−] is the skewness in the frequency distribution of thetap interval and indicates the degree of distortion of the frequencydistribution from the normal distribution. (40) A “standard deviation ofa local tap interval” [sec] is the standard deviation of three adjacenttap intervals. (41) A “slope (attenuation rate) of an approximate curveof the tap interval” [−] is the slope of a curve approximating the tapinterval. This slope mainly indicates a change in the tap interval dueto fatigue for the measurement time.

The feature amount [phase difference] has feature amount parametersindicated by the following identification numbers (42) to (45). (42)“Average of a phase difference” [deg] is the average value of a phasedifference in the waveform of both hands. The phase difference is anindex value that indicates the deviation of a finger tap of the lefthand from the right hand as an angle in a case in which one cycle of thefinger tap of the right hand is set to 360 degrees. In a case in whichthere is no deviation, the phase difference is 0 degrees. As the valueof (42) or (43) becomes larger, the deviation of both hands becomeslarger and more unstable. (43) A “standard deviation of the phasedifference” [deg] is a standard deviation related to the phasedifference. (44) “Similarity between both hands” [−] is a valueindicating correlation when the time lag is 0 in a case in which across-correlation function is applied to the waveforms of the left andright hands. (45) A “time lag at which the similarity between both handsis the maximum” [sec] is a value indicating the time lag at which thecorrelation of (44) is the maximum.

The feature amount [marker following] has feature amount parametersindicated by the following identification numbers (46) to (47). (46)“Average of delay time from a marker” [sec] is an average value relatedto the delay time of the finger tap with respect to the time indicatedby a periodic marker. The marker corresponds to stimulation, such asvisual stimulation, auditory stimulation, or tactile stimulation. Thisparameter value is based on the time when two fingers are in a closedstate. (47) A “standard deviation of the delay time from the marker”[sec] is a standard deviation related to the delay time.

[Detection of Abnormal Data without Using DB]

The non-DB-using abnormal data detection unit 14A performed by theabnormal data detection unit 14 of the abnormal data processing system 1will be described. The non-DB-using abnormal data detection unit 14Adetermines whether or not there is an abnormality only from themeasurement data without referring to the individual-subject DB 45A andthe multiple-subject DB 45B. Specifically, the following abnormalitydetection items are exemplified. The abnormal data detection unit 14detects the acquired data as abnormal data in a case in which there is amismatch between the original characteristics of data and thecharacteristics of the acquired data. The acquired measurement data orthe above-described various feature amounts obtained from themeasurement data can be used for the detection. The abnormal datadetection unit 14 that has detected abnormal data corresponding to theabnormal detection item performs a process corresponding to the detectedabnormal data.

FIG. 10 illustrates an example of a signal waveform obtained whenabnormal data is detected.

(E1) In Case in which User Continuously Measures Same Task by Mistake

In a case in which a plurality of data items corresponding to the sametask are input, the data is detected as abnormal data. As acorresponding process, the user is asked whether of the first and seconddata items is used on the screen of the terminal device 4 and selectsdata. Alternatively, considering that the practice effect is obtainedfrom the second measurement due to repetitive measurement, it may bedetermined that the first measurement that does not include the practiceeffect is always used. In addition, considering that the firstmeasurement is likely to fail because the user is unfamiliar withmeasurement, it may be determined that the second measurement is alwaysused. Further, the better of the first measurement and the secondmeasurement may be used with reference to the feature amounts stored inthe analysis evaluation data 43.

(E2) In Case in which Two-Handed Finger Tap is Selected by Mistake whenOne-Handed Finger Tap is Measured

This is a case in which the user performs movement with the intention ofmeasuring a one-handed finger tap, but, in practice, a two-handed fingertap is selected and measurement data is recorded on the system. For themeasurement data of each hand, the time for which no movement isperformed (hereinafter, referred to as a “movement non-execution time”)is calculated. The movement non-execution time is obtained by using theabove-mentioned feature amounts. For example, a time period for which(2) the “total movement distance” per unit time is equal to or less thana predetermined value TDc is defined. In addition to this definition,(14) the “total energy” or (33) the “number of taps” may be equal to orless than a predetermined value. In a case in which the movementnon-execution time is equal to or greater than a predetermined value Tcfor only one hand, it is erroneously determined that a task is performedwith only one hand even though the task has been performed with bothhands and the data is detected as abnormal data. For example, Tc can bepredetermined to be two-thirds of the measurement time. In this case,assuming that the measurement time is 15 seconds, abnormal data isdetected when the movement non-execution time is 10 seconds or more. Assuch, when the abnormal data is detected, the data of the handcorresponding to the movement non-execution time is ignored and theabnormal data is treated as the measurement data of a one-handed taskincluding the data of only the other hand. In addition, the system mayask the user or the administrator whether to process the data on thescreen of the terminal device 4 for confirmation, without automaticallyprocessing the data.

(E3) In Case in which One-Handed Finger Tap is Selected by Mistake whenTwo-Handed Finger Tap is Measured

This is a case in which the user performs movement with the intention ofmeasuring a two-handed finger tap, but, in practice, a one-handed fingertap is selected and measurement data is recorded on the system. As willbe described below, on a task measurement screen illustrated FIG. 12 ,in a case in which a one-handed finger tap is performed, only thewaveform of one hand is presented to the user. However, since themeasurement device 3 has acquired the measurement data of both hands,the measurement data of both hands is stored in the background. Themovement non-execution time is calculated for each hand in themeasurement data of both hands as described in the previous item. In acase in which the movement non-execution time for both hands is lessthan the predetermined value Tc, it can be determined that the user hasperformed the two-handed task. Then, data including the data of the handthat has not been presented to the user is treated as the measurementdata of the two-handed task. In addition, the system may ask the user orthe administrator whether to process the data on the screen forconfirmation, without automatically processing the data.

(E4) In Case in which Two-Handed Alternating Free-Run is Selected byMistake at Time of Measurement of Two-Handed Simultaneous Free-Run

This is a case in which the user performs movement with the intention ofmeasuring two-handed simultaneous free-run, but the measurement system 2instructs the user to perform two-handed alternating free-run. Among thefeature amounts stored in the analysis evaluation data 43 a, a featureamount for evaluating the cooperation of both hands is used in order todetect this abnormality. For example, (42) the “average of a phasedifference” is 0° in an ideal two-handed simultaneous free-run in whichthe movement of both hands is completely simultaneous and is 180° in anideal two-handed alternating free-run in which the movement of bothhands is completely alternate. Therefore, in a case in which (42) the“average of a phase difference” is less than a predetermined value (forexample, 90°), even though the two-handed alternating free-run isselected, the user can be regarded as performing movement with theintention of the two-handed simultaneous free-run. That is, it isconsidered that the two-handed alternating free-run has been selected bymistake at the time of the measurement of the two-handed simultaneousfree-run, and the task data 42A is changed to the measurement data ofthe two-handed simultaneous free-run after the measurement. In thiscase, the system may ask the user whether to process the data on thescreen for confirmation, without automatically processing the data. Asan example in which other feature values are used, in a case in which(44) the “similarity between both hands” is equal to or greater than apredetermined value (for example, 0), the movement may be considered asthe two-handed simultaneous free-run. In a case in which the absolutevalue of (45) the “time lag at which the similarity between both handsis the maximum” is less than a predetermined value (for example, (34)the “average tap interval”×0.25), the movement may be considered as thetwo-handed simultaneous free-run. In some cases, although the userintends to perform the two-handed simultaneous free-run, the user is notable to move both hands at the same time and measurement data close tothe two-handed alternating free-run is obtained. In this case, thesystem may ask the user or the administrator whether to process the dataon the screen for confirmation, without automatically processing thedata as abnormal data.

(E5) In Case in which Two-Handed Simultaneous Free-Run is Selected byMistake at Time of Measurement of Two-Handed Alternating Free-Run

This is a case in which the user performs movement with the intention ofmeasuring two-handed alternating free-run, but the measurement system 2instructs the user to perform two-handed simultaneous free-run, contraryto the previous paragraph. It is possible to perform determination onthe basis of the feature amount of a finger tap, similarly to theprevious paragraph. In a case in which (42) the “average of a phasedifference” is equal to or greater than a predetermined value (forexample, 90° which is an intermediate value between an ideal value of 0°in the two-handed simultaneous free-run and an ideal value of 180° inthe two-handed alternating free-run), even though the two-handedsimultaneous free-run is selected, the user is likely to performmovement with the intention of the two-handed alternating free-run. Thatis, it is considered that the two-handed simultaneous free-run has beenselected by mistake at the time of the measurement of the two-handedalternating free-run, and the task data 42A is changed to the two-handedsimultaneous free-run after the measurement. As an example in whichother feature values are used, in a case in which (44) the “similaritybetween both hands” is less than a predetermined value (for example, 0),the movement may be considered as the two-handed simultaneous free-run.In a case in which (45) the “time lag at which the similarity betweenboth hands is the maximum” is equal to or greater than a predeterminedvalue (for example, (34) the “average tap interval”×0.25), the movementmay be considered as the two-handed simultaneous free-run. In somecases, although the user intends to perform the two-handed alternatingfree-run, the user moves both hands at the same time and measurementdata close to the two-handed simultaneous free-run is obtained. In thiscase, the system may ask the user or the administrator whether toprocess the data on the screen for confirmation, without automaticallyprocessing the data as abnormal data.

(E6) In Case in which Two Fingers are Crossed During Measurement

This is a case in which the distance between two fingers is an abnormalvalue due to the cross of the thumb and the index finger during themeasurement of a finger tapping movement. In a case in which a magneticsensor is used as the motion sensor 20, the distance between two fingersmay be estimated to be a very large value for the time period for whichtwo fingers are crossed due to the nature of the magnetic sensor, asillustrated in FIG. 10(a). A feature value representing the amplitude ofthe waveform among the feature amounts stored in the analysis evaluationdata 43 is used in order to detect the abnormality. For example, (1) the“maximum amplitude of the distance” is greater than a predeterminedvalue (for example, 20 cm greater than the distance between two fingersof many people) or the maximum value of the distance between two fingerswhich has been measured before measurement, it can be considered thatthe abnormality in which two fingers are crossed has occurred. Inaddition, a time period for which the feature amount is greater than themaximum value of the distance between two fingers in the measurementdata 42B (raw waveform data) before the feature amounts are calculatedcan be extracted to specify the time period for which abnormality hasoccurred. Since the phenomenon that two fingers are crossed is also apart of the nature of the finger tapping movement, it may be better notto exclude the data without considering it as abnormal data. In thiscase, the system may ask the user or the administrator whether toprocess the data on the screen for confirmation, without automaticallyprocessing the data as abnormal data.

(E7) In Case in which Motion Sensor is Detached from Finger DuringMeasurement

This is a case in which the distance between two fingers is an abnormalvalue due to the detachment of the motion sensor from the finger duringmeasurement. When the motion sensor is detached, the distance betweentwo fingers is estimated to be a very large value as illustrated in FIG.10(b). As in the previous paragraph, in a case in which a feature amountrepresenting the amplitude of the waveform, such as (1) the “maximumamplitude of the distance”, is greater than a predetermined value or themaximum value of the distance between two fingers and is maintaineduntil the end of measurement, it can be considered that the motionsensor has been detached from the finger. Whether or not this state ismaintained until the end of the measurement can be determined byextracting the time period for which the feature amount is larger thanthe maximum value of the distance between two fingers in the measurementdata, as in the previous paragraph. In addition, when it is determinedthat the motion sensor has been detached from the finger even though apredetermined measurement time has not elapsed, the system mayimmediately end the measurement in real time and prompt re-measurement.

(E8) In Case in Which Movement Is Started During Measurement Time

This is a case in which the user starts movement during the measurementtime, without correctly understanding a signal to start measurement. Asillustrated in FIG. 10(c), in a case in which there is a movementnon-execution time at the beginning of the measurement, it can bedetermined that this abnormality has occurred. The determination ofwhether or not there is a movement non-execution time can be implementedby the above-described method. In addition, the measurement time may bedivided into a predetermined number (for example, 5) of segments and thefeature amount of a finger tap may be calculated in each segment. In acase in which the feature amount is compared between the segments andthere is a segment in which the value of the feature amount is clearlydifferent from that in other segments, it may be determined that thisabnormality has occurred. For example, a measurement time of 15 secondscan be divided into five 3-second segments. When (33) the “number oftaps” is calculated for each of the segments, it is assumed that valuesof 0, 5, 5, 5, and 4 are obtained as the values of the numbers of tapsin order from the first segment. In this case, when the presence orabsence of a segment that is separated by N standard deviations or more(for example, N=2) from the average value of five feature values in thedirection in which the magnitude of the movement decreases is checked,the number of taps in the first segment is 0. In this way, it is alsopossible to determine whether or not there is a movement non-executiontime.

(E9) In Case in which Movement is Ended During Measurement Time

This is a case in which the user ends movement by mistake before the endof measurement. As illustrated in FIG. 10(d), in a case in which thereis a movement non-execution time at the end of measurement, it can bedetected that this abnormality has occurred. The determination ofwhether or not there is a movement non-execution time can be implementedby the above-described method. In addition, as in the previousparagraph, the determination may be implemented by a method whichdivides the measurement time into N segments and calculates the movementnon-execution time. Further, when it is determined that movement hasended during a predetermined measurement time even though thepredetermined measurement time has not elapsed, the system mayimmediately end the measurement in real time and prompt re-measurement.

(E10) In Case in which Movement is Suspended During Measurement Time

This is a case in which movement is suspended due to, for example, theentanglement of cables during measurement. As illustrated in FIG. 10(e),in a case in which there is a movement non-execution time duringmeasurement, it can be detected that this abnormality has occurred. Thedetermination of whether or not there is a movement non-execution timecan be implemented by the above-described method. In addition, as in theprevious paragraph, the determination may be implemented by a methodwhich divides the measurement time into N segments and calculates themovement non-execution time. Further, when it is determined thatmovement has been suspended during a predetermined measurement time eventhough the predetermined measurement time has not elapsed, the systemmay immediately end the measurement in real time and promptre-measurement.

[Detection of Abnormal Data Using DB]

The DB-using abnormal data detection unit 14B performed by the abnormaldata detection unit 14 of the abnormal data processing system 1 will bedescribed. For example, in a case in which new data of the user X isacquired, the DB-using abnormal data detection unit 14B determineswhether or not the new data is abnormal with reference to theindividual-subject DB 45A and the multiple-subject DB 45B in which thepast data of the user X has been stored. Specifically, the followingabnormality detection items are exemplified.

(E11) In Case in which Nature of Movement is Changed by User's Intention

This is a case in which the user intentionally insincerely performsmovement or misunderstands the instruction of a task, which results in alow movement performance. This can be detected in a case in which thedegree of divergence of the performance from that stored in the past DB(individual-subject DB) of the user or the past DB of many subjects(multiple-subject DB) is large as a result of the comparison with thepast DB. A method for calculating the degree of divergence will bedescribed below.

(E12) In Case in which Nature of Movement is Changed by User's PhysicalConditions

This includes a case in which the performance of movement deterioratesdue to the deterioration of a brain function or a movement function orextreme fatigue and a case in which the performance of movement isimproved by the effects of medication and rehabilitation. These can bedetected when the degree of divergence of the performance from thatstored in the past DB (individual-subject DB) of the user is large as aresult of the comparison with the past DB. A method for calculating thedegree of divergence will be described below.

(E13) In Case in which Someone Impersonates User

This is a case in which someone impersonates the user to change thenature of movement. This can be detected when the degree of divergenceof the nature of movement from that stored in the past DB(individual-subject DB) of the user is large as a result of thecomparison with the past DB. A method for calculating the degree ofdivergence will be described below.

<<Calculation of Degree of Divergence>>

A method for calculating the degree of divergence will be described. Thedegree of divergence is calculated by the individual-subject DBdivergence degree calculation unit 14Ba, the multiple-subject DBdivergence degree calculation unit 14Bb, and the composite divergencedegree calculation unit 14Bc.

First, a divergence degree calculation method common to theindividual-subject DB divergence degree calculation unit 14Ba and themultiple-subject DB divergence degree calculation unit 14Bb will bedescribed. N (N≥1) feature amounts of the finger tapping movement areselected and a data distribution in an N-dimensional space is generatedfor the DB (the individual-subject DB 45A or the multiple-subject DB45B). It is assumed that the average of the data distribution is M(=[m1, m2, . . . , mN]) and the standard deviation thereof is Σ (=[σ1,σ2, . . . , σN]). In addition, it is assumed that the data which is anabnormal data detection target is A (=[a1, a2, . . . , aN]). In thiscase, the degree of divergence is calculated as d=|(A−M)/Σ|. Here, ∥indicates the absolute value of a vector (the square root of the sum ofsquares). When d is larger than a predetermined value dc, it isdetermined that the degree of divergence from the DB is sufficientlylarge. For example, when dc=1 is established, it can be said that themeasurement data is out of 68.3% of the data close to the average in theDB. Similarly, when dc=2 is established, it can be said that themeasurement data is out of 95.5% of the data. When dc=3 is established,it can be said that the measurement data is out of 99.7% of the data.That is, dc may be set to a small value in a case in which the userwants to strictly detect abnormal data and may be set to a large valuein a case in which the user wants to roughly detect abnormal data. Thedegree of divergence may be defined by a method other than theabove-described method and may be any index indicating divergence fromthe DB.

In the above-described calculation of the degree of divergence, thefeature amounts of the finger tapping movement are used without anychange. However, the feature amounts may be processed to create a newindex. For example, principal component analysis may be applied to allof the feature amounts and N principal components having a highcontribution rate may be used.

A change in the feature amounts of the finger tapping movement isobserved to specify whether the performance of the movement hasdeteriorated or improved. For example, when (2) the “total movementdistance”, (14) the “total energy”, or (33) the “number of taps” is lessthan the average of the individual-subject DB, it can be understood thatthe performance of the movement has deteriorated. Conversely, when thesefeatures are greater than the average of the individual-subject DB, itcan be understood that the performance of movement has been improved.

In addition, in order to specify the cause of a change in theperformance of the movement, a screen on which the user inputs, forexample, whether or not the brain function or the movement function hasdeteriorated, whether or not the user feels fatigue, and whether or notthe user receives a treatment, such as medication or rehabilitationbefore measurement may be provided. In this case, the input content maybe referred to as the reason for abnormality detection (which will bedescribed below in FIG. 15 ) when the performance of the movement hasdeteriorated or improved as compared to the individual-subject DB 45A.In this way, it is possible to increase the persuasiveness of the reasonwhy the abnormal data is detected to the user.

<<Combination of Degree of Divergence>>

With the above-described method, the individual-subject DB divergencedegree calculation unit 14Ba and the multiple-subject DB divergencedegree calculation unit 14Bb can calculate the degree of divergence ofthe measurement data from the individual-subject DB 45A or themultiple-subject DB 45B. From this point of view, the individual-subjectDB 45A makes it possible to detect abnormal data in which thecharacteristics of the data of the user have been reflected. Therefore,it is desirable to use the individual-subject DB 45A in order toaccurately detect abnormal data. Specifically, as illustrated in FIG.11(a), data 1 and data 2 represented by ★ have the same degree ofdivergence from the individual-subject DB 45A of a user A and arecorrectly detected as abnormal data. However, in a case in which theuser has already performed measurement many times, theindividual-subject DB 45A can be used. However, in a case in which theuser has performed the measurement for the first time or only a smallnumber of times, it is necessary to use the multiple-subject DB 45Bsince the individual-subject DB 45A has not been sufficientlyaccumulated. In FIGS. 11 to 13 , a two-dimensional space formed by twofeature amounts is considered and the DB and abnormal data areschematically illustrated. However, the number of feature amount may beone or three or more and a multidimensional space corresponding to thenumber of feature amounts used is considered.

In a case in which the multiple-subject DB 45B is used, there is anadvantage that it is easy to prepare a DB since the data of users otherthan the user can be stored in advance. However, the multiple-subject DB45B is an aggregate of the data of many users and the characteristics ofthe data of the users are not reflected in the multiple-subject DB 45B.Therefore, there is a disadvantage that the accuracy of detectingabnormal data is likely to be lower than that in a case in which theindividual-subject DB 45A is used. Specifically, as illustrated in FIG.11(b), data 1 and data 2 represented by ★ have different degrees ofdivergence from the multiple-subject DB 45B. There is a problem that thedata 2 is correctly detected as abnormal data and the data 1 is notdetected as abnormal data. From the awareness of the above-mentionedproblem, it is considered that a technique which complements theadvantages and disadvantages of the individual-subject DB 45A and themultiple-subject DB 45B and uses the two DBs together is needed.

Therefore, either the degree of divergence of the individual subject DB45A or the degree of divergence of the multiple-subject DB 45B is notselected and used, but the degrees of divergence are combined tocalculate a new degree of divergence (composite degree of divergence).It is considered that the use of the composite degree of divergencemakes it possible to perform abnormal data detection in which theadvantages and disadvantages when either the individual-subject DB 45Aor the multiple-subject DB 45B is used are complemented.

The composite degree of divergence is calculated by the compositedivergence degree calculation unit 14Bc. The composite degree ofdivergence ds is calculated using an individual-subject DB reliabilitycoefficient c, an individual-subject DB divergence degree d1, and amultiple-subject DB divergence degree d2, as illustrated in FIG. 12(a).The individual-subject DB reliability coefficient c is an indexindicating the degree of reliability of the individual-subject DB 45Aand is a value of 0.0 to 1.0 as illustrated in FIG. 12(b). When thenumber of data items in the individual-subject DB 45A is 0, theindividual-subject DB reliability coefficient c is 0.0 and graduallyapproaches 1.0 as the number of data items increases. An expressionindicating the relationship between the individual-subject DBreliability coefficient c and the number of data items k may be anyexpression as long as the individual-subject DB reliability coefficientc increases as the number of data items increases. For example, asillustrated in FIG. 12(b), c=1/(1+exp(−α(k−β)))+γ can be set by asigmoid function (for example, α=0.1, β=50). Here, when theindividual-subject DB is trusted in a stage in which the number of dataitems is small, α may be set to a large value. When theindividual-subject DB is trusted in a stage in which the number of dataitems is large, α may be set to a small value. In addition, β and γ maybe adjusted such that c is 0 when k is 0. The composite degree ofdivergence ds is defined as ds=d1×c+d2×(1.0−c) using theindividual-subject DB reliability coefficient c defined as describedabove. When ds is larger than the predetermined value dc, it isdetermined that the degree of divergence from the DB is sufficientlylarge. That is, the individual-subject DB reliability coefficient c is aweight for the individual-subject DB divergence degree d1 and increasesas the number of data items in the individual-subject DB 45A increases.A temporal attenuation divergence degree which will be described belowmay be used as the individual-subject DB divergence degree d1 and themultiple-subject DB divergence degree d2.

<<Calculation of Degree of Divergence Considering Time Series>>

A method for calculating the degree of divergence (hereinafter, referredto as a temporal attenuation divergence degree) calculated consideringthe time-series relationship of the data in the DB when theindividual-subject DB divergence degree calculation unit 14Ba calculatesthe degree of divergence from the individual-subject DB 45A will bedescribed. The data of the individual-subject DB 45A is accumulated overtime by the periodical measurement of the user. However, the healthconditions of the user change daily due to, for example, aging, adecline in cognitive functions, and a decline in movement functions.Therefore, when abnormal data is detected, it is considered that themore recent data has higher reliability and the older data has lowerreliability. Specifically, as illustrated in FIG. 13(a), when the datain the individual-subject DB 45A changes over time in the order of1→2→3, 4a and 4 b have the same degree of divergence from the average ofthe DB. However, 4 a needs to be determined as abnormal data since it isfar from 3 which is the latest data and 4 b does not need to bedetermined as abnormal data since it is close to 3 which is the latestdata.

Therefore, as illustrated in FIG. 13(b), a temporal attenuationdivergence degree calculated by increasing a weight for the most recentdata and decreasing a weight for the older data is used as theindividual-subject DB divergence degree d1. Specifically, the data inthe individual-subject DB 45A is defined as Bi (=[bi1, bi2, . . . ,biN], i=1 to k (the number of data items in the individual-subject DB45A)) and past data reliability qi=p^(ti) is defined according to thetime ti going back from the acquisition of new data (0.0<p<1.0). Then,the average of the data distribution in the individual-subject DB 45A isdefined as M=q1B1+q2B2+ . . . + qkBk. When M is defined in this way, themore recent data has higher reliability and the older data has lowreliability. Furthermore, the standard deviation Σ of the datadistribution in the DB can be defined as Σ=((q1B1−M)²+(q2B2−M)²+ . . .+(qkBk−M)²)/k using M. As described above, the individual-subject DBdivergence degree d1 is calculated as d=|(A−M)/Σ| using M and Σ.

The degree of divergence considering the time series has been describedin consideration of the individual-subject DB 45A. However, the samecalculation as described above can be performed in the multiple-subjectDB 45B. That is, the multiple-subject DB divergence degree d2 may becalculated by calculating the degree of divergence considering the timeseries for each individual subject in the multiple-subject DB 45B andcalculating the average of the degrees of divergence.

[Determination of Abnormal Data Processing]

The abnormal data processing determination unit 15 processes theabnormal data detected by the non-DB-using abnormal data detection unit14A and the DB-using abnormal data detection unit 14B of the abnormaldata processing system 1. Abnormal items of the non-DB-using abnormaldata detection unit 14A and the DB-using abnormal data detection unit14B performed by the abnormal data detection unit 14 of the abnormaldata processing system 1 are stored in a table format in the abnormalitydetection reason-processing correspondence table 50B in the managementtable 50 illustrated in FIG. 14 . This may be given in advance when theabnormal data processing system 1 is constructed or may be set by theadministrator of the abnormal data processing system 1. A plurality ofprocesses are described in a process column. In practice, one of theprocesses is selected and set. The abnormal data processingdetermination unit 15 performs a process corresponding to theabnormality detection item detected by the non-DB-using abnormal datadetection unit 14A and the DB-using abnormal data detection unit 14B onthe basis of the abnormality detection reason-processing correspondencetable 50B.

[Execution of Abnormal Data Processing]

The abnormal data processing execution unit 16 executes the abnormaldata processing content 46 determined by the abnormal data processingdetermination unit 15. In a case in which the measurement data is notused, it is assumed that the data is not registered in theindividual-subject DB 45A and the multiple-subject DB 45B. In a case inwhich re-measurement is performed, the abnormal data processing content46 of re-measurement is transmitted to the terminal device 4 through thecommunication unit 105. The terminal device 4 receives the abnormal dataprocessing content 46 and performs re-measurement in cooperation withthe measurement device 3. In a case in which the system inquires theuser of data handling, the system transmits the content of the inquiryto the terminal device 4 through the communication unit 105. Theterminal device 4 displays the content of the inquiry on the screen andthe user sees the screen and responds to the inquiry. The content of theuser's response is transmitted to the abnormal data processing systemthrough the communication unit 105. The abnormal data processingexecution unit 16 executes a process on the basis of the content of theuser's response.

[Display Screen (1)—Menu]

FIG. 15 illustrates an example of a menu screen which is a serviceinitial screen as an example of the display screen of the terminaldevice 4. The menu screen includes, for example, a user informationcolumn 1501, an operation menu column 1502, and a setting column 1503.

The user can input and register user information in the user informationcolumn 1501. In a case in which there is user information that has beeninput in, for example, an electronic medical record, the information maybe associated with the user information. Examples of the userinformation that can be input include a user ID, a name, a birth date orage, sex, a dominant hand, a disease/symptom, and a memo. The dominanthand can be selected and input from, for example, the right hand, theleft hand, both hands, and unknown. The disease/symptom may be selectedand input from, for example, options in a list box or may be input inany text. For example, in a case in which this system is used in ahospital, not the user but the doctor may input the user information,instead of the user. The abnormal data processing system may be appliedeven in a case in which no user information is registered.

Operation items of the functions provided by the service are displayedin the operation menu column 1502. The operation items include, forexample, “calibration”, “measurement of finger movement”, “abnormal datadetection and processing”, and “end”. In a case in which the“calibration” is selected, a process related to the above-mentionedcalibration, that is, the adjustment of, for example, the motion sensor20 with respect to the user's finger is performed. In addition, thestate of whether or not the adjustment has been performed is displayed.In a case in which the “measurement of finger movement” is selected, thescreen is changed to a task measurement screen for measuring a fingermovement task, such as a finger tap. In a case in which the “abnormaldata detection and processing” is selected, abnormality is detected fromthe measurement data, the detection result of the abnormal data isdisplayed, and the screen is changed to a screen for processing thedetected abnormal data. In a case in which the “end” is selected, thisservice is ended.

User setting can be performed in the setting column 1503. For example,in a case in which there is a type of abnormality detection item thatthe user, the measurer, or the administrator wants to detect, the user,the measurer, or the administrator can select the abnormality detectionitem from the options and can set the selected abnormality detectionitem. In addition, it is possible to select a process corresponding toeach abnormality detection item. Further, it is possible to set anabnormal data detection threshold value. These settings are transmittedto the abnormal data processing system 1 through the communication unit105 and the abnormal data processing system 1 detects and processesabnormal data with reference to the settings designated here.

[Display Screen (2)—Task Measurement]

FIG. 16 illustrates a task measurement screen as another example. Taskinformation is displayed on this screen. For example, a graph 1600 inwhich the horizontal axis indicates time and the vertical axis indicatesthe distance between two fingers is displayed for each of the left andright hands. Other teaching information for explaining the content of atask may be output to the screen. For example, a video region forexplaining the content of the task with video and audio may be provided.The screen has operation buttons, such as “measurement start”,“re-measurement”, “measurement end”, and “save (register)”, which can beselected by the user. The user selects “measurement start” and performstask movement according to task information on the screen. Themeasurement device 3 measures the task movement to obtain a waveformsignal. The terminal device 4 displays a measured waveform 1602corresponding to the waveform signal under measurement on the graph 1600in real time. After movement, the user selects “measurement end”. Whenconfirming the selection, the user selects “save (register)”. Themeasurement device 3 transmits the measurement data to the abnormal dataprocessing system 1.

[Display Screen (3)—Evaluation Result]

FIG. 17 illustrates an evaluation result screen as another example.Analysis evaluation result information of a task is displayed on thisscreen. After a task is analyzed and evaluated, this screen isautomatically displayed. In this example, five feature amounts A to E ofa finger tapping movement are displayed in a graph in a radar chartformat. A solid frame 1701 indicates an analysis evaluation result afterthe current task measurement. A feature amount display method is notlimited to the method of displaying the feature amounts in the radarchart and may be a method of displaying the feature amounts in apredetermined format such as a graph. The feature amount may beconverted and displayed in a format such as a performance score (forexample, a perfect score of 100). In addition to the graph of thefeature amounts, for example, an evaluation comment related to theanalysis evaluation result may be displayed. The analysis evaluationunit 13 creates an evaluation comment. For example, a message “(B) and(E) are good” is displayed. The screen has operation buttons such as“overwrite past results”, “proceed to abnormal data detection andprocessing”, and “end”. The abnormal data processing system changes thescreen to an abnormal data detection and processing screen in a case inwhich the “proceed to abnormal data detection and processing” isselected and changes the screen to the initial screen in a case in whichthe “end” is selected.

[Display Screen (3)—Abnormal Data Detection and Processing]

FIG. 18 illustrates an abnormal data detection and processing screen asanother example. The abnormal data detection result 44 and the abnormaldata processing content 46 transmitted from the abnormal data processingsystem 1 are displayed on this screen. This screen is displayed afterthe “abnormal data detection and processing” button illustrated in FIG.11 or the “progress to abnormal data detection and processing” buttonillustrated in FIG. 18 is pressed. Basic information, such as userinformation or measurement data information, is displayed and then thedetection result of abnormal data is displayed on this screen. In a casein which abnormality is detected in the measurement data, “abnormal” isdisplayed. In a case in which no abnormality is detected, “noabnormality” is displayed. Further, in a case in which measurement datais determined to be “abnormal”, the reason for detecting the abnormaldata is illustrated. The abnormal data detection reason is included inthe abnormal data detection result 44 transmitted from the abnormal dataprocessing system 1. In FIG. 18 , a message “because movement was notperformed in a measurement time of 0 to 3 seconds” is illustrated as anexample of the non-DB-using abnormal data detection 14A. The waveform ofthe finger tapping movement is presented below the abnormal datadetection reason to visually explain the abnormal data detection reason.In addition, a recommended process corresponding to the abnormal datadetection reason is illustrated below the waveform. In FIG. 18 ,“re-measure” is given as an example of the corresponding process. Acorrespondence table between the abnormal data detection reason and theprocess is recorded on the abnormality detection reason-processingcorrespondence table 50B in the management table 50. The user, themeasurer, or the administrator selects a “perform the recommendedprocess” button when performing the recommended process illustrated inFIG. 18 and selects a “do not perform the recommended process” buttonwhen not wanting to perform the recommended process. In addition, theabnormal data processing system 1 may automatically perform the processand notify the result of the process after the process, without allowingthe user, the measurer, or the administrator to select whether or not toperform the process.

FIG. 19 illustrates another example of the abnormal data detection andprocessing screen. In FIG. 19 , as an example of the DB-using abnormaldata detection 14B, the abnormal data detection reason is “because thedata has deviated from the past individual DB”. “Do not register data inDB” is given as an example of the recommended process corresponding tothe abnormal data detection reason.

[Effects]

According to the abnormal data processing system 1 of Embodiment 1, boththe individual-subject DB 45A and the multiple-subject DB 45B are usedto achieve highly accurate abnormal data processing. The reason is thata decrease in accuracy when the number of data items in theindividual-subject DB 45A is insufficient can be covered by increasing aweight for the multiple-subject DB 45B and a decrease in accuracy due toincapability to reflect an individual differences when themultiple-subject DB 45B is used can be covered by increasing a weightfor the individual-subject DB 45A.

Embodiment 2

An abnormal data processing system according to Embodiment 2 of theinvention will be described with reference to FIGS. 20 to 26 . The basicconfiguration of Embodiment 2 is the same as that of Embodiment 1.Hereinafter, portions of the configuration of Embodiment 2 which aredifferent from those of the configuration of Embodiment 1 will bedescribed.

[System (2)]

FIG. 20 illustrates a human data measurement system including theabnormal data processing system according to Embodiment 2. The humandata measurement system is provided in, for example, a hospital, afacility for the elderly, or the user's home. The human data measurementsystem according to Embodiment 2 The abnormal data processing systemuses a measurement system which is a tablet-type finger tap movementmeasurement system. The measurement system includes a terminal device 5which is a tablet terminal. In Embodiment 2, movement measurement andinformation display are performed using a touch panel provided in theterminal device 5. Embodiment 2 corresponds to an aspect in which themeasurement function of the measurement device 3 and the displayfunction of the terminal device 4 according to Embodiment 1 areintegrated into one terminal device 5. The terminal device 5 may be adevice that is installed in a facility or a device that is owned by theuser.

The terminal device 5 includes, for example, a control unit 501, astorage unit 502, a communication unit 505, and a touch panel 510 whichare connected to each other through a bus. The touch panel 510 includesa display unit 511 and a touch sensor 512. The display unit 511 is, forexample, a liquid crystal display unit or an organic EL display unit andhas a display screen. The touch sensor 512 is, for example, a capacitivetype and is provided in a region corresponding to the display screen.The touch sensor 512 detects, as an electrical signal, a change incapacitance according to the approach or contact state of a finger onthe display screen and outputs the detected signal to a touch detectionunit 521.

The control unit 501 controls the entire terminal device 5, includes,for example, a CPU, a ROM, and a RAM, and implements a data processingunit 500 that performs abnormal data processing and the like, on thebasis of software program processing. The configuration of the dataprocessing unit 500 is substantially the same as that in Embodiment 1.The control unit 501 further includes the touch detection unit 521 and ameasurement processing unit 522. The control unit 501 implements, forexample, a function of obtaining measurement data through the touchpanel 510, a function of processing and analyzing the measurement data,and a function of outputting information to a display screen of thedisplay unit 511 of the touch panel 510. The touch detection unit 521performs a process of detecting the approach or contact state of theuser's finger and the movement state of the finger on the display screenas touch position coordinates and a time-series signal thereof on thebasis of the detected signal from the touch sensor 512. The measurementprocessing unit 522 measures the position and movement of the finger onthe display screen as a waveform signal and obtains measurement data,using the detected information of the touch detection unit 521. Themeasurement data corresponds to measurement data 42B. The dataprocessing unit 500 performs abnormal data detection and abnormal dataprocessing determination on the basis of the measurement data, using thesame process as that in Embodiment 1, and displays the result of theprocess on the display screen of the display unit 511. In addition, forexample, the data processing unit 500 creates analysis evaluation dataand displays an evaluation screen on the display screen of the displayunit 511. The data processing unit 500 has the same functions as thedata processing unit illustrated in FIG. 2 , such as a user informationmanagement unit 11 and an abnormal data detection unit 14 including anon-DB-using abnormal data detection unit 14A and a DB-using abnormaldata detection unit 14B. The storage unit 502 has the same functions asthe second storage unit 102 and stores, for example, user information41, task data 42A, measurement data 42B, analysis evaluation data 43, anabnormal data detection result 44, an individual-subject DB 45A, amultiple-subjects DB 45B, a management table 50, and abnormal dataprocessing content 46.

[Example (1) of Movement and Display Screen]

FIG. 21 illustrates a method in which a finger tapping movement isperformed on a display screen 210 of the terminal device 5. The terminaldevice 5 may provide a task using this method. In this method, thecontrol unit 501 displays regions 211 in which two target fingers ofboth hands are placed on a background region of the display screen 210.In this case, the target two fingers are, for example, the thumb as afirst finger and the index finger as a second finger. The user placesthe two fingers of each hand so as to come into contact with the region211 or to be close to the region 211. In this example, when the fingersare moved, the state in which the finger touches the region 211 of thedisplay screen is basically maintained, which depends on, for example,the touch sensor 512. The user performs a finger tap of opening andclosing two fingers in the region 211. The terminal device 5 measuresthe finger tapping movement through, for example, the touch sensor 512and obtains measurement data such as a waveform signal as inEmbodiment 1. Arrows indicate the movement 212 of the first finger andthe movement 213 of the second finger on the region 211. As a distance Lbetween two fingertips, a distance L1 on the left hand side and adistance L2 on the right hand side are shown.

FIG. 22 illustrates a waveform signal of the distance L between twofingers as an example of the measurement data corresponding to thefinger tapping movement illustrated in FIG. 21 . The horizontal axisindicates the elapsed time t [sec] and the vertical axis indicates thedistance L(t) [mm] for each elapsed time t. A waveform portion 221indicates a state in which the finger is separated from the region 211to some extent. In a case in which the waveform is broken in this way, acontinuous waveform can be obtained by interpolating the waveform in astate in which the finger is on the region 211. The terminal device 5extracts feature amounts on the basis of the measurement data, performsabnormal data detection, abnormal data processing determination, andabnormal data processing execution, and presents the results, using themethod, as in Embodiment 1.

[Example (2) of Movement and Display Screen]

FIG. 23 illustrates a reaching method as another example of the fingertapping movement and the display screen. The terminal device 5 mayprovide a task using the reaching method. (a) of FIG. 23 illustratescross leaching. First, a FIG. 231 of the initial position is displayedon the display screen 210 of the terminal device 5 and measurement isstarted in a state in which a target finger, for example, the indexfinger is placed on the FIG. 231 of the initial position. After themeasurement is started, a target FIG. 232 corresponding to a marker, forexample, a cross is displayed on the display screen 210. The controlunit 501 displays the FIG. 232 at different positions, for example, at apredetermined cycle. The user performs a finger tap so as to extend thefingers following the position of the FIG. 232 . In this example, astate in which the user taps a position 233 that deviates from thecenter position of the FIG. 232 with the finger is shown. There is adistance E corresponding to the deviation between the center position ofthe target FIG. 232 and the tap or touch position 233. The terminaldevice 5 calculates, for example, the distance E or a delay time TD asone of the feature amounts on the basis of the measurement data. Thedelay time TD is a period from the time when the target FIG. 232 isdisplayed in a standby state in which the finger is placed on the FIG.231 of the initial position to the time when the finger touches thetarget FIG. 232 .

(b) of FIG. 23 illustrates circular reaching. A circular region isdisplayed as a target FIG. 234 . Similarly, the user performs a fingertap on the circular region of the FIG. 234 . As a feature amount, forexample, the distance between the center position of the FIG. 234 andthe tap position is extracted.

[Example (3) of Movement and Display Screen]

FIG. 24 illustrates a continuous touch method as another example of thefinger tapping movement and the display screen. The terminal device 5may provide a task or practice using the continuous touch method. FIG.24(a) illustrates a one-handed continuous touch. A FIG. 241 for thetouch of the thumb of the left hand, for example, a circular region isdisplayed at one position on the display screen 210, for example, in thevicinity of the lower left corner of the display screen 210. The usercontinuously touches the displayed FIG. 241 with a finger. In a case inwhich the FIG. 241 is not displayed, the user removes the finger fromthe FIG. 241 . The control unit 501 controls the display of the FIG. 241. For example, the display and non-display of the FIG. 241 are switchedat a predetermined cycle and the FIG. 241 is displayed a predeterminednumber of times. In addition to the display of the FIG. 241 , forexample, auditory stimulation may be given as teaching information. Asfeature amounts, for example, the number of touches, touch interval, andtouch delay time of the FIG. 241 are extracted.

FIG. 24(b) illustrates a two-handed simultaneous continuous touch. FIG.242 indicating the touch positions of the target fingers of the left andright hands are displayed at two positions on the display screen 210.The user continuously touches the displayed FIG. 242 with both hands atthe same timing. Similarly, a two-handed alternating continuous touch ispossible. In this case, the control unit 501 performs switching suchthat the left and right FIG. 242 are alternately displayed. The usertouches these FIG. 242 with the left and right hands at alternatetimings. As a feature amount, for example, a phase difference betweenthe touches of the left and right FIG. 242 is extracted.

As another example of the movement, for example, auditory stimulus maybe output as the teaching information without displaying the figure. Forexample, two types of sounds may be output at a predetermined cycle at atouch time and a non-touch time.

[Example (4) of Movement and Display Screen]

FIG. 25 illustrates a tapping method according to light as anotherexample of the finger tapping movement and the display screen. FIG.25(a) illustrates a one-handed tap. A tapping FIG. 251 for the targetfinger of the left hand and a FIG. 252 serving as visual stimulationlight for indicating the tapping timing of the FIG. 251 are displayed onthe display screen 210. The control unit 501 blinks the FIG. 252 suchthat the display and non-display of the FIG. 252 are switched. The usertaps the tapping FIG. 251 at the timing when the FIG. 252 is displayed.As another example of the movement, a sound for auditory stimulation maybe output instead of the FIG. 252 for visual stimulation or a continuoustouch method may be used. As a feature amount, for example, there is atime lag between the time when simulation is periodically generated andthe tap or touch time. The time lag corresponds to a delay time from thetime when the FIG. 252 is displayed to the time when the FIG. 251 istapped. FIG. 25(b) similarly illustrates the case of a two-handedsimultaneous tap. Two tapping FIG. 251 are provided on the left andright sides and two FIG. 252 for visual stimulation are displayed on theleft and right sides so as to be blinked at the same timing. Similarly,in the case of a two-handed alternating tap, the control unit 501displays the two FIG. 252 on the left and right sides so as to beblinked at alternate timings.

[Example (5) of Movement and Display Screen]

FIG. 26 illustrates a five-finger tapping method as another example ofthe finger tapping movement and the display screen. The terminal device5 may provide a task using the five-finger tapping method. In thismethod, five fingers of the target hand are used. The terminal device 5displays tapping FIG. 261 for five fingers of each of both hands, thatis, a total of ten fingers on the background region of the displayscreen 210. First, the user places five fingers so as to touch thedisplay screen 210. The terminal device 5 automatically adjusts and setsthe display positions of the FIG. 261 on the basis of the detection ofthe touch positions. The terminal device 5 controls the display of theFIG. 261 at each position. The terminal device 5 changes the FIG. 261 atthe position to be tapped to a specific display state (represented by,for example, a black circle) and changes the other FIG. 261 at thepositions not to be tapped to another display state. The terminal device5 controls the switching of the display state of the FIG. 261 . The usertaps the FIG. 261 with the finger according to the display of the FIG.261 to be tapped.

[Feature Amounts]

Examples of the feature amounts unique to Embodiment 2 are as follows.

Feature amount parameters related to the reaching method are as follows.(2-1) An “average value of a delay time from target display” [sec] is anaverage value related to the delay time. (2-2) A “standard deviation ofdelay time from target display” [sec] is the standard deviation of thedelay time. (2-3) An “average value of a position error with respect toa target” [mm] is the average value of the distance E. (2-4) A “standarddeviation of a position error with respect to a target” [mm] is astandard deviation related to the distance E.

Feature amount parameters related to the one-handed continuous touchmethod are as follows: (2-5) the “number of taps” [−]; (2-6) a “tapinterval average” [sec]; (2-7) a “tap frequency” [Hz]; (2-8) a “tapinterval standard deviation [sec]; (2-9) a “tap interval variationcoefficient” [−]; (2-10) a “tap interval variation” [mm²]; (2-11)“skewness of a tap interval distribution” [−]; (2-12) a “standarddeviation of a local tap interval” [sec]; and (2-13) a “tap intervalattenuation rate”. The definition of each feature amount is the same asthat in Embodiment 1.

Feature amount parameters related to the two-handed continuous touchmethod are as follows. (2-14) “Average of a phase difference” [deg] is,for example, an average value of the phase difference of a two-handedtouch. (2-15) A “standard deviation of a phase difference” [deg] is thestandard deviation of the phase difference.

Feature amount parameters related to a touch or tapping method accordingto light or auditory stimulation. (2-16) An “average value of time lagwith respect to stimulus” [sec] is the average value of theabove-described time lag. (2-17) A “standard deviation of time lag withrespect to stimulus” [deg] is the standard deviation of theabove-described time lag.

[Detection of Abnormal Data without Using DB]

The non-DB-using abnormal data detection unit 14A performed by theabnormal data detection unit 14 of the data processing unit 500 will bedescribed. Similarly to Embodiment 1, the non-DB-using abnormal datadetection unit 14A determines whether or not there is an abnormalityonly from the measurement data without referring to the DB. Basically,the same abnormality detection items as those in Embodiment 1 are givenas examples. However, only the items unique to this embodiment will bedescribed below. Among the abnormality items described in Embodiment 1,(E2) does not occur in the case of a task using both hands in thisembodiment since the positions touched with both hands are visuallyindicated. In addition, since (E6) is the phenomenon unique to themagnetic sensor, (E6) may not be considered in this embodiment in whichthe screen is touched.

(E4) In Case in which Two-Handed Alternating Tap is Selected by Mistakeat Time of Measurement of Two-Handed Simultaneous Tap

This is a case in which the user performs movement with the intention ofmeasuring a two-handed simultaneous tap, but, in practice, a two-handedalternating tap is selected and measurement data is recorded on thesystem. Among the feature amounts stored in the analysis evaluation data43, a feature amount for evaluating the cooperation of both hands isused in order to detect this abnormality. For example, (2-14) the“average of a phase difference” is 0° in an ideal two-handedsimultaneous tap in which the movement of both hands is completelysimultaneous and is 180° in an ideal two-handed alternating tap in whichthe movement of both hands is completely alternate. Therefore, in a casein which (2-14) the “average of a phase difference” is less than apredetermined value (for example, 90°), even though the two-handedalternating tap is selected, the user can be regarded as performingmovement with the intention of the two-handed simultaneous tap. That is,it is considered that the two-handed alternating tap has been selectedby mistake at the time of the measurement of the two-handed simultaneoustap, and the task data 42A is changed to the measurement data of thetwo-handed simultaneous tap after the measurement.

(E5) In Case in which Two-Handed Simultaneous Tap is Selected by Mistakeat Time of Measurement of Two-Handed Alternating Tap

This is a case in which the user performs movement with the intention ofmeasuring a two-handed alternating tap, but, in practice, a two-handedsimultaneous tap is selected and measurement data is recorded on thesystem, contrary to the previous paragraph. It is possible to performdetermination on the basis of the feature amounts of a finger tap,similarly to the previous paragraph. For example, (2-14) the “average ofa phase difference” is equal to or greater than a predetermined value(for example, 90°), even though the two-handed simultaneous tap isselected, the user is likely to perform movement with the intention ofthe two-handed alternating tap. That is, it is considered that thetwo-handed simultaneous tap has been selected by mistake at the time ofthe measurement of the two-handed alternating tap, and the task data 42Ais changed to the measurement data of the two-handed simultaneous tapafter the measurement.

(E7) In Case in which Finger Comes Off Predetermined Position DuringMeasurement

This is a case in which the finger touches a position that deviates froma predetermined position designated on the screen during measurement. Itcan be determined that this abnormality has occurred in a case in whichthere is a predetermined period of movement-non-execution time.Specifically, for example, the movement non-execution time can beevaluated as the time when (2-5) the “number of taps” is 0. In addition,even before a predetermined measurement time ends, measurement may beended in real time and re-measurement may be prompted. Further, themeasurement data may not be detected as abnormal data and a touch aroundthe predetermined position may also be detected. Then, visual or audibleguidance may be provided such that the user can return to apredetermined correct position in a case in which the touch positiondeviates from the predetermined position. The movement non-executiontime in (E8), (E9), and (E10) may be evaluated as the time when (2-5)the “number of taps” is 0 as in this abnormality detection item.

[Effects]

According to the abnormal data processing system of Embodiment 2, boththe individual-subject DB 45A and the multiple-subject DB 45B are usedto achieve highly accurate abnormal data processing, similarly toEmbodiment 1. In Embodiment 2, particularly, it is not necessary toprovide, for example, the motion sensor 20. Therefore, it is possible toreduce the time and effort required for the user to perform measurement.

Embodiment 3

An abnormal data processing system according to Embodiment 3 of theinvention will be described with reference to FIGS. 27 to 29 . The basicconfiguration of Embodiment 3 is the same as that of Embodiment 1.Hereinafter, portions of the configuration of Embodiment 2 which aredifferent from those of the configuration of Embodiment 1 will bedescribed.

[System (3)]

FIG. 27 illustrates an abnormal data processing system according toEmbodiment 3. The abnormal data processing system includes a server 6 ofa service provider and systems 7 in a plurality of facilities, which areconnected to each other through a communication network 8. Thecommunication network 8 or the server 6 may include a cloud computingsystem. The functions of the abnormal data processing system accordingto Embodiment 3 are shared by the terminal devices 4 of the systems 7and the server 6. The sharing will be described below.

The facilities can include various types of facilities, such as ahospital, a medical examination center, a public facility, anentertainment facility, and a user's home. The facility is provided withthe system 7. Examples of the system 7 in the facility include a system7A of a hospital H1 and a system 7B of a hospital H2. For example, eachof the system 7A and the system 7B of the hospitals includes themeasurement device 3 and the terminal device 4 forming the samemeasurement system 2 as that in Embodiment 1. The systems 7 may have thesame configuration or may have different configurations. The system 7 ofthe facility may include, for example, a hospital electronic medicalrecord management system. The measurement device of the system 7 may bea dedicated terminal.

The server 6 is a device under the control of the service provider. Theserver 6 has a function of providing the same abnormal data processingservice as that in the abnormal data processing system 1 according toEmbodiment 1 as an information processing service to the facilities andthe users. The server 6 provides service processing to the measurementsystem using a client-server method. The server 6 has, for example, auser management function in addition to these functions. The usermanagement function is a function of registering, accumulating, andmanaging, for example, the user information, measurement data, andanalysis evaluation data, of user groups obtained through the systems 7of a plurality of facilities in the DB. The terminal device 5 accordingto Embodiment 3 does not need have a function of processing abnormaldata and has a measurement function using a touch panel and a displayfunction of displaying, for example, the detection results of abnormaldata generated by the server 6.

[Server]

FIG. 28 illustrates the configuration of the server 6. The server 6includes a control unit 601, a storage unit 602, an input unit 603, anoutput unit 604, and a communication unit 605 which are connected toeach other through a bus. The input unit 603 is a portion that inputs anoperation of, for example, the administrator of the server 6. The outputunit 604 is a portion that displays a screen to, for example, theadministrator of the server 6. The communication unit 605 has acommunication interface and performs a communication process with thecommunication network 8. The storage unit 602 stores a DB 640. The DB640 may be managed by, for example, a DB server other than the server 6.

The control unit 601 controls the entire server 6, includes, forexample, a CPU, a ROM, and a RAM, and implements a data processing unit600 that performs, for example, abnormal data detection and abnormaldata processing determination on the basis of software programprocessing. The data processing unit 600 includes a user informationmanagement unit 11, a task processing unit 12, an analysis andevaluation unit 13, an abnormal data detection unit 14, an abnormal dataprocessing determination unit 15, an abnormal data processing executionunit 16, and a result output unit 17. Unlike Embodiment 1, the abnormaldata detection unit 14 does not include the non-DB-using abnormal datadetection unit 14A and includes only the DB-using abnormal datadetection unit 14B.

The user information management unit 11 registers user informationrelated to a user group of the systems 7 in a plurality of facilities asuser information 41 in the DB 640 and manages the user information. Theuser information 41 includes, for example, an attribute value, usagehistory information, and user setting information for each user. Theusage history information includes information on the results of eachuser using the abnormal data processing service in the past.

[Server Management Information]

FIG. 29 illustrates an example of the data configuration of the userinformation 41 managed by the server 6 in the DB 640. A table of theuser information 41 includes, for example, a user ID, a facility ID, anin-facility user ID, sex, age, a disease, a severity score, a symptom,history information. The user ID is unique identification information ofthe user in this system. The facility ID is identification informationof the facility in which the system 7 is installed. In addition, forexample, the communication address of the measurement device of eachsystem 7 is managed. The in-facility user ID is user identificationinformation in a case in which the user identification informationmanaged in the facility or the system 7 is present. That is, the user IDand the in-facility user ID are managed so as to be associated with eachother. For the disease item or the symptom item, a value indicating adisease or a symptom selected and input by the user or a value diagnosedby, for example, the doctor in the hospital is stored. The severityscore is a value indicating a degree related to a disease.

The history information item is information for managing the pastservice usage and abnormal data processing results of the user. Forexample, the information of the date and time when the user used eachservice is stored in time series in the history information item.Further, each data item in a case in which practice is performed at thattime, that is, data, such as the above-described measurement data,analysis evaluation data, abnormal data detection result, and abnormaldata processing content, is stored in the history information item. Theinformation of the address where each data item has been stored may bestored in the history information item.

[Sharing of Abnormal Data Detection Between Local and Server]

In Embodiment 1, the abnormal data detection unit 14 is configured toperform both the non-DB-using abnormal data detection unit 14A and theDB-using abnormal data detection unit 14B. In contrast, in thisembodiment, the non-DB-using abnormal data detection unit 14A isperformed by the local terminal device 4 of the system 7 and theDB-using abnormal data detection unit 14B is performed by the server 6.The reason for this sharing is that the individual-subject DB 45A andthe multiple-subject DB 45B are formed from the data aggregated from aplurality of systems 7 (7A, 7B, . . . ) and the server is suitable forabnormal data detection using the DBs. On the other hand, it ispreferable that the local terminal device 4 performs abnormal datadetection that does not require the DBs in order to perform the abnormaldata detection as soon as possible. Since the local terminal device 4detects abnormal data, it is possible to detect abnormal data in realtime and to immediately issue a re-measurement instruction in a case inwhich abnormality occurs during measurement. Further, in a case in whichthe server is not always connected to the network, it is possible toprevent the loss of time required for transmitting data to the serverand waiting for the detection results of abnormal data.

The method that shares the abnormal data detection function between theterminal device 4 and the server 6 on the basis of whether or not to usethe DB as described above has been described. However, other sharingmethods may be used. For example, when many users visit the hospital H1and a large-scale DB can be constructed, the DB-using abnormal datadetection unit 14B may be implemented by the terminal device 4. Inaddition, in a case in which the authority to collectively change thesettings of the abnormality detection reason-processing correspondencetable 50B of the management table 50 is given to the administrator ofthe system, the non-DB-using abnormal data detection unit 14A may alsobe implemented by the server 6.

[Effects]

According to the abnormal data processing system of Embodiment 3, boththe individual-subject DB 45A and the multiple-subject DB 45B are usedto achieve highly accurate abnormal data processing, similarly toEmbodiment 1. In addition, since the individual-subject DB 45A and themultiple-subject DB 45B are managed by the server, it is considered thatdata from many facilities can be aggregated to construct a large-scaleDB and more accurate abnormal data detection can be achieved. Inaddition, since the abnormal data detection function is shared betweenthe local terminal device 4 and the server 6, it is possible to detectabnormal data without loss of time.

The invention has been specifically described above on the basis of theembodiments. However, the invention is not limited to theabove-described embodiments and various modifications of the inventioncan be made without departing from the scope and spirit of theinvention.

The invention is not limited to the above-described embodiments andincludes various modifications. For example, a part of the configurationof one embodiment can be replaced with the configuration of anotherembodiment and the configuration of one embodiment can be added to theconfiguration of another embodiment. Further, for a part of theconfiguration of each embodiment, it is possible to add, delete, orreplace the configuration of other embodiments.

INDUSTRIAL APPLICABILITY

It is possible to use an information processing service technique.

REFERENCE SIGNS LIST

-   1 Abnormal data processing system-   2 Measurement system-   3 Measurement device-   4 Terminal device

The invention claimed is:
 1. An abnormal data processing system thatdetects whether or not new data is abnormal and processes the new data,comprising: a storage unit that stores a multiple-subject database (DB)in which data of a plurality of subjects is accumulated and anindividual-subject DB in which data of an individual subject isaccumulated; an individual-subject DB divergence degree calculation unitthat calculates an individual-subject DB divergence degree which is adegree of divergence of the new data from the individual-subject DB; amultiple-subject DB divergence degree calculation unit that calculates amultiple-subject DB divergence degree which is a degree of divergence ofthe new data from the multiple-subject DB; and a composite divergencedegree calculation unit that calculates a composite degree of divergenceobtained by combining the individual-subject DB divergence degree andthe multiple-subject DB divergence degree, using a number of data itemsin the individual-subject DB, wherein it is determined whether or notthe new data is abnormal on a basis of the composite degree ofdivergence.
 2. The abnormal data processing system according to claim 1,wherein the composite divergence degree calculation unit uses anindividual-subject DB reliability coefficient which increases as thenumber of data items in the individual-subject DB increases and weightsthe individual-subject DB divergence degree with the individual-subjectDB reliability coefficient to calculate the composite degree ofdivergence.
 3. The abnormal data processing system according to claim 1,further comprising: an abnormal data processing determination unit thatdetermines a process in a case in which the new data is determined to beabnormal; and an abnormal data processing execution unit that performsthe process.
 4. The abnormal data processing system according to claim1, wherein the individual-subject DB divergence degree calculation unithas a temporal attenuation divergence degree calculation function thatcalculates a difference in measurement time between the new data andeach data item in the individual-subject DB, calculates past datareliability whose attenuation becomes larger as the difference inmeasurement time becomes larger, and calculates the individual-subjectDB divergence degree from each data item and the past data reliability.5. The abnormal data processing system according to claim 1, furthercomprising: a non-DB-using abnormal data detection unit that determineswhether or not the new data is abnormal on a basis of the new data or afeature amount obtained from the new data, without using themultiple-subject DB and the individual-subject DB.
 6. The abnormal dataprocessing system according to claim 5, wherein the new data is fingermovement data, and the non-DB-using abnormal data detection unit uses atleast one of a waveform amplitude, a movement non-execution time, andtwo-hand cooperation calculated from the finger movement data as thefeature amount and detects whether or not the feature amount deviatesfrom a predetermined numerical range.
 7. The abnormal data processingsystem according to claim 5, wherein the abnormal data processing systemincludes a local that includes a measurement device acquiring the newdata and a server that is connected to the local through a communicationnetwork, in the local, the non-DB-using abnormal data detection unitdetermines whether or not the new data is abnormal, and in the server,the composite divergence degree calculation unit calculates thecomposite degree of divergence and determines whether or not the newdata is abnormal on the basis of the composite degree of divergence. 8.An abnormal data processing method that detects whether or not new dataacquired by an input unit is abnormal and processes the new data, usingthe input unit, an output unit, a control unit, and a storage unit, themethod comprising: storing a multiple-subject DB in which data of aplurality of subjects is accumulated and an individual-subject DB inwhich data of an individual subject is accumulated in the storage unit;calculating an individual-subject DB divergence degree which is a degreeof divergence of the new data from the individual-subject DB;calculating a multiple-subject DB divergence degree which is a degree ofdivergence of the new data from the multiple-subject DB; calculating acomposite degree of divergence using the individual-subject DBdivergence degree and the multiple-subject DB divergence degree, on abasis of a number of data items in the individual-subject DB; anddetermining whether or not the new data is abnormal on a basis of thecomposite degree of divergence.
 9. The abnormal data processing methodaccording to claim 8, wherein the composite degree of divergence iscalculated by increasing a weight for the individual-subject DBdivergence degree as the number of data items in the individual-subjectDB increases.
 10. The abnormal data processing method according to claim8, wherein an abnormality detection reason-processing correspondencetable for determining a process in a case in which the new data isdetermined to be abnormal is stored in the storage unit, and in a casein which the new data is determined to be abnormal, a process based onthe abnormality detection reason-processing correspondence table isperformed.
 11. The abnormal data processing method according to claim 8,wherein a difference in measurement time between the new data and eachdata item in the individual-subject DB is calculated, past datareliability whose attenuation becomes larger as the difference inmeasurement time becomes larger is calculated, and theindividual-subject DB divergence degree is calculated from each dataitem and the past data reliability.
 12. The abnormal data processingmethod according to claim 8, further comprising: determining whether ornot the new data is abnormal on a basis of the new data or a featureamount obtained from the new data, without using the multiple-subject DBand the individual-subject DB.
 13. The abnormal data processing methodaccording to claim 12, wherein the new data is finger movement data, atleast one of a waveform amplitude, a movement non-execution time, andtwo-hand cooperation calculated from the finger movement data is used asthe feature amount, and it is detected whether the feature amountdeviates from a predetermined numerical range to determine whether thenew data is abnormal.
 14. The abnormal data processing method accordingto claim 12, wherein a local that includes a measurement deviceacquiring the new data and a server that is connected to the localthrough a communication network are used, the local determines whetheror not the new data is abnormal on the basis of the new data or afeature amount obtained from the new data, without using themultiple-subject DB and the individual-subject DB, and the serverdetermines whether or not the new data is abnormal, using themultiple-subject DB and the individual-subject DB.
 15. The abnormal dataprocessing method according to claim 12, wherein the data of theindividual-subject DB is a subset of the data of the multiple-subjectDB.